{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":10385,"databundleVersionId":298493,"sourceType":"competition"}],"dockerImageVersionId":31089,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# 导入库","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nimport scipy.ndimage\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport os\nimport joblib\nfrom sklearn.metrics import accuracy_score, roc_auc_score, classification_report, confusion_matrix, log_loss\nfrom catboost import CatBoostClassifier, Pool\nfrom sklearn.model_selection import StratifiedKFold,train_test_split, RandomizedSearchCV, PredefinedSplit","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T14:06:00.445154Z","iopub.execute_input":"2025-09-16T14:06:00.446107Z","iopub.status.idle":"2025-09-16T14:06:02.860543Z","shell.execute_reply.started":"2025-09-16T14:06:00.446067Z","shell.execute_reply":"2025-09-16T14:06:02.859523Z"}},"outputs":[],"execution_count":1},{"cell_type":"markdown","source":"# 导入数据","metadata":{}},{"cell_type":"code","source":"train=pd.read_csv(\"/kaggle/input/santander-customer-transaction-prediction/train.csv\").drop(['ID_code'],axis=1)\ntest=pd.read_csv(\"/kaggle/input/santander-customer-transaction-prediction/test.csv\").drop(['ID_code'],axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T14:06:13.277696Z","iopub.execute_input":"2025-09-16T14:06:13.278417Z","iopub.status.idle":"2025-09-16T14:06:34.527920Z","shell.execute_reply.started":"2025-09-16T14:06:13.278270Z","shell.execute_reply":"2025-09-16T14:06:34.526455Z"}},"outputs":[],"execution_count":2},{"cell_type":"markdown","source":"# 找到伪造test数据集","metadata":{}},{"cell_type":"markdown","source":"我们发现在test中存在部分由原来数据合成的伪造test数据，通过统计每个数在该列中出现的次数，如果一整行数据都是非唯一值，那么说明该数据是由其他列拼凑起来的，默认其结果为0","metadata":{}},{"cell_type":"code","source":"te_=test.values\n#如果一行数据中的每一列的数据在其他行中都有出现过说明这行数据是伪造数据\nunique_samples = []\nunique_count = np.zeros_like(te_)\nfor feature in tqdm(range(te_.shape[1])):\n    _, index_, count_ = np.unique(te_[:, feature], return_counts=True, return_index=True)\n    unique_count[index_[count_ == 1], feature] += 1\n\n# Samples which have unique values are real the others are fake\nreal_samples_indexes = np.argwhere(np.sum(unique_count, axis=1) > 0)[:, 0]\nsynthetic_samples_indexes = np.argwhere(np.sum(unique_count, axis=1) == 0)[:, 0]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T14:06:34.529482Z","iopub.execute_input":"2025-09-16T14:06:34.529765Z","iopub.status.idle":"2025-09-16T14:06:41.437701Z","shell.execute_reply.started":"2025-09-16T14:06:34.529736Z","shell.execute_reply":"2025-09-16T14:06:41.436767Z"}},"outputs":[{"name":"stderr","text":"100%|██████████| 200/200 [00:06<00:00, 29.74it/s]\n","output_type":"stream"}],"execution_count":3},{"cell_type":"code","source":"X_test=test.iloc[real_samples_indexes,:]\nX_fake=test.iloc[synthetic_samples_indexes,:]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T14:06:56.285882Z","iopub.execute_input":"2025-09-16T14:06:56.286217Z","iopub.status.idle":"2025-09-16T14:06:56.476363Z","shell.execute_reply.started":"2025-09-16T14:06:56.286168Z","shell.execute_reply":"2025-09-16T14:06:56.475345Z"}},"outputs":[],"execution_count":4},{"cell_type":"code","source":"features = [c for c in train.columns if c not in ['ID_code', 'target']]\nX_train=train.drop(['target'],axis=1)\ntarget_train=train['target']\nX_all = pd.concat([X_train, X_test])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T14:06:58.392020Z","iopub.execute_input":"2025-09-16T14:06:58.392387Z","iopub.status.idle":"2025-09-16T14:06:58.746035Z","shell.execute_reply.started":"2025-09-16T14:06:58.392360Z","shell.execute_reply":"2025-09-16T14:06:58.745110Z"}},"outputs":[],"execution_count":5},{"cell_type":"markdown","source":"# 预处理","metadata":{}},{"cell_type":"markdown","source":"## 删除低相关性和低重要性的特征（弃用）","metadata":{}},{"cell_type":"code","source":"drop_vars = [100,10,96,17,7,161,126,136,38,98,103,117,124,158,27,30,185]\n\nvar_len = 200 - len(drop_vars)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T14:45:45.951256Z","iopub.execute_input":"2025-09-16T14:45:45.951608Z","iopub.status.idle":"2025-09-16T14:45:45.957053Z","shell.execute_reply.started":"2025-09-16T14:45:45.951588Z","shell.execute_reply":"2025-09-16T14:45:45.955959Z"}},"outputs":[],"execution_count":20},{"cell_type":"code","source":"drop_cols = X_train.columns[drop_vars]\n\n# 删除对应列\nX_train = X_train.drop(columns=drop_cols)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T14:47:30.704413Z","iopub.execute_input":"2025-09-16T14:47:30.704763Z","iopub.status.idle":"2025-09-16T14:47:30.802837Z","shell.execute_reply.started":"2025-09-16T14:47:30.704729Z","shell.execute_reply":"2025-09-16T14:47:30.801845Z"}},"outputs":[],"execution_count":21},{"cell_type":"code","source":"X_test = X_test.drop(columns=drop_cols)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T14:47:55.069707Z","iopub.execute_input":"2025-09-16T14:47:55.069999Z","iopub.status.idle":"2025-09-16T14:47:55.131888Z","shell.execute_reply.started":"2025-09-16T14:47:55.069977Z","shell.execute_reply":"2025-09-16T14:47:55.130729Z"}},"outputs":[],"execution_count":22},{"cell_type":"markdown","source":"# 翻转（弃用）","metadata":{}},{"cell_type":"code","source":"# reverse_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 11, 15, 16, 18, 19, 22, 24, 25, 26,\n#                 27, 29, 32, 35, 37, 40, 41, 47, 48, 49, 51, 52, 53, 55, 60, 61,\n#                 62, 65, 66, 67, 69, 70, 71, 74, 78, 79, 82, 84, 89, 90, 91, 94,\n#                 95, 96, 97, 99, 103, 105, 106, 110, 111, 112, 118, 119, 125, 128,\n#                 130, 133, 134, 135, 137, 138, 140, 144, 145, 147, 151, 155, 157,\n#                 159, 161, 162, 163, 164, 167, 168, 170, 171, 173, 175, 176, 179,\n#                 180, 181, 184, 185, 187, 189, 190, 191, 195, 196, 199,\n                \n#                 ]\n\n# for j in reverse_list:\n#     X[:, j] *= -1","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 简单计数编码（弃用）","metadata":{}},{"cell_type":"code","source":"# def count_encode_and_merge(df: pd.DataFrame, target_column) -> pd.DataFrame:\n#     \"\"\"\n#     对每个特征列进行计数编码，并将计数编码后的列与原始数据合并。\n\n#     参数:\n#     ----\n#     df : DataFrame\n#         输入的原始数据表（不包含目标列）。\n#     target_column : str, optional\n#         目标列的名称（默认值为 None）。如果提供，则从 `df` 中删除目标列。\n\n#     返回:\n#     ----\n#     DataFrame\n#         合并后的数据，包括原始数据和计数编码后的列。\n#     \"\"\"\n#     # 如果指定了 target_column，删除该列\n#     if target_column:\n#         df = df.drop(columns=[target_column])\n    \n#     # 对每一列进行计数编码\n#     count_encoded_df = df.apply(lambda col: col.map(col.value_counts()))\n    \n#     # 将计数编码后的列与原数据合并\n#     df_encoded = pd.concat([df, count_encoded_df.add_suffix('_count')], axis=1)\n    \n#     return df_encoded","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# train_x_encoded = count_encode_and_merge(train_x, target_column=None)\n# test_encoded = count_encode_and_merge(test, target_column=None)\n# train_x_encoded.columns = train_x_encoded.columns.astype(str)\n# test_encoded.columns = test_encoded.columns.astype(str)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 计数加高斯平滑密度以及偏差","metadata":{}},{"cell_type":"code","source":"sigma_fac = 0.001\nsigma_base = 4\n\neps = 0.00000001\n\ndef get_count(X_all, X_fake):\n    features_count = np.zeros((X_all.shape[0], len(features)))\n    features_density = np.zeros((X_all.shape[0], len(features)))\n    features_deviation = np.zeros((X_all.shape[0], len(features)))\n\n    features_count_fake = np.zeros((X_fake.shape[0], len(features)))\n    features_density_fake = np.zeros((X_fake.shape[0], len(features)))\n    features_deviation_fake = np.zeros((X_fake.shape[0], len(features)))\n    \n    sigmas = []\n\n    for i,var in enumerate(tqdm(features)):\n        X_all_var_int = (X_all[var].values * 10000).round().astype(int)\n        X_fake_var_int = (X_fake[var].values * 10000).round().astype(int)\n        lo = X_all_var_int.min()\n        X_all_var_int -= lo\n        X_fake_var_int -= lo\n        hi = X_all_var_int.max()+1\n        counts_all = np.bincount(X_all_var_int, minlength=hi).astype(float)\n        zeros = (counts_all == 0).astype(int)\n        before_zeros = np.concatenate([zeros[1:],[0]])\n        indices_all = np.arange(counts_all.shape[0])\n        # Geometric mean of twice sigma_base and a sigma_scaled which is scaled to the length of array \n        sigma_scaled = counts_all.shape[0]*sigma_fac\n        sigma = np.power(sigma_base * sigma_base * sigma_scaled, 1/3)\n        sigmas.append(sigma)\n        counts_all_smooth = scipy.ndimage.filters.gaussian_filter1d(counts_all, sigma)\n        deviation = counts_all / (counts_all_smooth+eps)\n        indices = X_all_var_int\n        features_count[:,i] = counts_all[indices]\n        features_density[:,i] = counts_all_smooth[indices]\n        features_deviation[:,i] = deviation[indices]\n        indices_fake = X_fake_var_int\n        features_count_fake[:,i] = counts_all[indices_fake]\n        features_density_fake[:,i] = counts_all_smooth[indices_fake]\n        features_deviation_fake[:,i] = deviation[indices_fake]\n        \n    features_count_names = [var+'_count' for var in features]\n    features_density_names = [var+'_density' for var in features]\n    features_deviation_names = [var+'_deviation' for var in features]\n\n    X_all_count = pd.DataFrame(columns=features_count_names, data = features_count)\n    X_all_count.index = X_all.index\n    X_all_density = pd.DataFrame(columns=features_density_names, data = features_density)\n    X_all_density.index = X_all.index\n    X_all_deviation = pd.DataFrame(columns=features_deviation_names, data = features_deviation)\n    X_all_deviation.index = X_all.index\n    X_all = pd.concat([X_all,X_all_count, X_all_density, X_all_deviation], axis=1)\n    \n    X_fake_count = pd.DataFrame(columns=features_count_names, data = features_count_fake)\n    X_fake_count.index = X_fake.index\n    X_fake_density = pd.DataFrame(columns=features_density_names, data = features_density_fake)\n    X_fake_density.index = X_fake.index\n    X_fake_deviation = pd.DataFrame(columns=features_deviation_names, data = features_deviation_fake)\n    X_fake_deviation.index = X_fake.index\n    X_fake = pd.concat([X_fake,X_fake_count, X_fake_density, X_fake_deviation], axis=1)    \n\n    features_count = features_count_names\n    features_density = features_density_names\n    features_deviation = features_deviation_names\n    return X_all, features_count, features_density, features_deviation, X_fake\n\nX_all, features_count, features_density, features_deviation, X_fake = get_count(X_all, X_fake)\nprint(X_all.shape)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 标准化","metadata":{}},{"cell_type":"code","source":"features_to_scale = [features, features_count]\n\nfrom sklearn.preprocessing import StandardScaler\n\ndef get_standardized(X_all, X_fake):\n    scaler = StandardScaler()\n    features_to_scale_flatten = [var for sublist in features_to_scale for var in sublist]\n    scaler.fit(X_all[features_to_scale_flatten])\n    features_scaled = scaler.transform(X_all[features_to_scale_flatten])\n    features_scaled_fake = scaler.transform(X_fake[features_to_scale_flatten])\n    X_all[features_to_scale_flatten] = features_scaled\n    X_fake[features_to_scale_flatten] = features_scaled_fake\n    return X_all, X_fake\n\nX_all, X_fake = get_standardized(X_all, X_fake)\n\nprint(X_all.shape)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_length=200000\nX_train = X_all.iloc[:train_length,:]\nX_test = X_all.iloc[train_length:,:]\ndel X_all\nimport gc\ngc.collect()\nprint(X_train.shape, X_test.shape)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 一些可视化函数","metadata":{}},{"cell_type":"code","source":"#查看划分的比例\ndef plot_class_distribution(y, title=\"Class Distribution\",save_path=None):\n    \"\"\"\n    可视化二分类数据的数量和比例\n    :param y: 二分类目标变量 (pd.Series 或 list)\n    :param title: 图表标题\n    \"\"\"\n    # 转换为 Series\n    if not isinstance(y, pd.Series):\n        y = pd.Series(y)\n\n    # 统计数量和比例\n    class_counts = y.value_counts()\n    class_percent = y.value_counts(normalize=True) * 100\n\n    # 合并到一个 DataFrame 方便展示\n    df = pd.DataFrame({\"Count\": class_counts, \"Percentage\": class_percent.round(2)})\n\n    # 绘制柱状图\n    plt.figure(figsize=(6, 4))\n    sns.barplot(x=df.index, y=df[\"Count\"], palette=\"Blues_d\")\n    \n    # 在柱子上显示数量和比例\n    for i, (count, pct) in enumerate(zip(df[\"Count\"], df[\"Percentage\"])):\n        plt.text(i, count + 0.5, f\"{count} ({pct}%)\", ha=\"center\")\n\n    plt.title(title)\n    plt.xlabel(\"Class\")\n    plt.ylabel(\"Count\")\n    plt.xticks(rotation=0)\n    plt.show()\n    if save_path:\n        plt.savefig(f\"{save_path}/{title}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T14:07:16.443018Z","iopub.execute_input":"2025-09-16T14:07:16.443369Z","iopub.status.idle":"2025-09-16T14:07:16.451148Z","shell.execute_reply.started":"2025-09-16T14:07:16.443344Z","shell.execute_reply":"2025-09-16T14:07:16.450285Z"}},"outputs":[],"execution_count":6},{"cell_type":"code","source":"def evaluate_binary_classifier(\n    y_true, y_pred, y_proba, model_name, model=None,\n    label_names=('Class 0', 'Class 1'), title_suffix='(Validation Set)', \n    save_results=True\n):\n    \"\"\"\n    二分类评估工具函数：打印Accuracy/AUC/分类报告，并绘制混淆矩阵\n    :param y_true: 真实标签（验证集）\n    :param y_pred: 预测标签（由模型predict得到）\n    :param y_proba: 正类概率（由模型predict_proba得到的[:,1]）\n    :param model: 训练好的模型\n    :param model_name: 模型名称（用于文件夹命名和保存模型）\n    :param label_names: 混淆矩阵坐标轴的类别名称\n    :param title_suffix: 图标题后缀，便于标注是验证集/测试集\n    :param save_results: 是否保存评估结果、混淆矩阵图和模型，默认保存\n    :return: 指标字典\n    \"\"\"\n    \n    # 创建保存目录\n    save_dir = f\"/kaggle/working/{model_name}_evaluation\"\n    if save_results and not os.path.exists(save_dir):\n        os.makedirs(save_dir)\n\n    # 评估指标计算\n    acc = accuracy_score(y_true, y_pred)\n    auc = roc_auc_score(y_true, y_proba)\n    \n    # 打印数值指标\n    print(f\"\\n=== {title_suffix} ===\")\n    print(f\"Accuracy: {acc:.4f}\")\n    print(f\"AUC: {auc:.4f}\")\n    print(\"\\n分类报告:\")\n    print(classification_report(y_true, y_pred))\n    \n    # 如果需要保存评估结果\n    if save_results:\n        # 保存评估指标到txt文件\n        with open(f\"{save_dir}/{title_suffix}_evaluation_results.txt\", \"w\") as f:\n            f.write(f\"=== {title_suffix} ===\\n\")\n            f.write(f\"Accuracy: {acc:.4f}\\n\")\n            f.write(f\"AUC: {auc:.4f}\\n\")\n            f.write(\"\\n分类报告:\\n\")\n            f.write(classification_report(y_true, y_pred))\n        \n        # 保存混淆矩阵图\n        plt.figure(figsize=(8, 6))\n        cm = confusion_matrix(y_true, y_pred)\n        sns.heatmap(\n            cm, annot=True, fmt='d', cmap='Blues',\n            xticklabels=label_names,\n            yticklabels=label_names\n        )\n        plt.title(f'Confusion Matrix {title_suffix}')\n        plt.ylabel('True Label')\n        plt.xlabel('Predicted Label')\n        plt.savefig(f\"{save_dir}/{title_suffix}_confusion_matrix.png\")\n        plt.close()\n\n        #保存标签比例\n        result = y_pred.astype(int)\n        plot_class_distribution(result, f\"{model_name} {title_suffix} Target Distribution\",save_dir)\n\n        if model is not None:\n        # 保存模型\n            joblib.dump(model, f\"{save_dir}/model_{title_suffix}.joblib\")\n    \n    # 总是展示混淆矩阵\n    plt.figure(figsize=(8, 6))\n    cm = confusion_matrix(y_true, y_pred)\n    sns.heatmap(\n        cm, annot=True, fmt='d', cmap='Blues',\n        xticklabels=label_names,\n        yticklabels=label_names\n    )\n    plt.title(f'Confusion Matrix {title_suffix}')\n    plt.ylabel('True Label')\n    plt.xlabel('Predicted Label')\n    plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T14:15:34.962116Z","iopub.execute_input":"2025-09-16T14:15:34.962489Z","iopub.status.idle":"2025-09-16T14:15:34.975875Z","shell.execute_reply.started":"2025-09-16T14:15:34.962466Z","shell.execute_reply":"2025-09-16T14:15:34.974632Z"}},"outputs":[],"execution_count":11},{"cell_type":"code","source":"def plot_feature_importance(\n    model, \n    feature_names=None, \n    top_n=20, \n    importance_type='gain', \n    title='Top Feature Importance',\n    save_path=None\n):\n    \"\"\"\n    通用特征重要性可视化函数（支持XGBoost、CatBoost和LightGBM）\n    :param model: 训练好的 XGBClassifier / CatBoostClassifier / LGBMClassifier\n    :param feature_names: 特征名列表（默认自动生成）\n    :param top_n: 展示前N个特征\n    :param importance_type: \n        - XGBoost: 'weight'/'gain'/'cover'\n        - LightGBM: 'split'/'gain'\n    :param title: 图标题\n    :param save_path: 保存路径（如 'feature_importance.png'），为 None 时不保存\n    \"\"\"\n\n    # ===== XGBoost =====\n    if hasattr(model, \"get_booster\"):  \n        plt.figure(figsize=(12, 8))\n        xgb.plot_importance(model, max_num_features=top_n, importance_type=importance_type)\n        plt.title(title)\n\n    # ===== CatBoost =====\n    elif hasattr(model, \"get_feature_importance\"):  \n        importances = model.get_feature_importance()\n        if feature_names is None:\n            feature_names = [f'feat_{i}' for i in range(len(importances))]\n        idx = np.argsort(importances)[::-1][:top_n]\n        plt.figure(figsize=(12, 8))\n        plt.barh([feature_names[i] for i in idx][::-1], np.array(importances)[idx][::-1])\n        plt.title(title)\n        plt.xlabel('Importance')\n        plt.ylabel('Feature')\n        plt.tight_layout()\n\n    # ===== LightGBM =====\n    elif hasattr(model, \"feature_importances_\"):  \n        importances = model.feature_importances_\n        if feature_names is None:\n            feature_names = [f'feat_{i}' for i in range(len(importances))]\n        idx = np.argsort(importances)[::-1][:top_n]\n        plt.figure(figsize=(12, 8))\n        plt.barh([feature_names[i] for i in idx][::-1], np.array(importances)[idx][::-1])\n        plt.title(title)\n        plt.xlabel('Importance')\n        plt.ylabel('Feature')\n        plt.tight_layout()\n\n    else:\n        raise ValueError(\"Unsupported model type: only XGBoost, CatBoost and LightGBM are supported.\")\n\n    # 保存或显示\n    if save_path:\n        save_dir = os.path.dirname(save_path)\n        if save_dir and not os.path.exists(save_dir):\n            os.makedirs(save_dir)\n        plt.savefig(save_path, dpi=300, bbox_inches='tight')\n        print(f\"特征重要性图已保存到: {save_path}\")\n        plt.show()\n        plt.close()\n        \n    else:\n        plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T14:07:22.385020Z","iopub.execute_input":"2025-09-16T14:07:22.385390Z","iopub.status.idle":"2025-09-16T14:07:22.396868Z","shell.execute_reply.started":"2025-09-16T14:07:22.385363Z","shell.execute_reply":"2025-09-16T14:07:22.395652Z"}},"outputs":[],"execution_count":8},{"cell_type":"markdown","source":"# 单独训练特征集成","metadata":{"execution":{"iopub.status.busy":"2025-09-15T06:18:39.884371Z","iopub.execute_input":"2025-09-15T06:18:39.884694Z","iopub.status.idle":"2025-09-15T06:18:40.401516Z","shell.execute_reply.started":"2025-09-15T06:18:39.884665Z","shell.execute_reply":"2025-09-15T06:18:40.400237Z"}}},{"cell_type":"markdown","source":"由于通过相关性计算发现每一列直接的相关性都极其低，所以我们采用了单独训练每个特征最后进行集成的策略，使得每个特征得到充分运用，同时避免伪交互的发生，在第二种编码的基础上在public上得到了0.92186的评分","metadata":{}},{"cell_type":"code","source":"params = {\n    'learning_rate': 0.08,  # 对应 LightGBM 中的 learning_rate\n    'depth': 2,  # 对应 LightGBM 中的 max_depth\n    'loss_function': 'Logloss',  # 对应 LightGBM 中的 metric\n    'iterations': 1000,  # 迭代次数\n    'thread_count': 8,  # 对应 LightGBM 中的 num_threads\n    'subsample': 1,  # 对应 LightGBM 中的 feature_fraction\n    'l2_leaf_reg': 2,  # 对应 LightGBM 中的 reg_alpha\n    'l1_leaf_reg': 0,  # 对应 LightGBM 中的 reg_lambda\n    'verbose': 1,  # 输出详细信息\n}\n\n# learning_rate\nlearning_rate_values = [0.06, 0.08, 0.12]\nlearning_rate_var = [0, 2, 2, 1, 2, 2, 2, 0, 1, 2, 0, 2, 2, 2, 0, 2, 2, 0, 2, 1, 2, 2, 2, 2, 2, 0, 1, 0, 2, 0, 0, 2, 0, 2, 2, 2, 1, 2, 0, 0, 2, 0, 0, 1, 2, 1, 2, 0, 0, 2, 1, 2, 2, 2, 2, 0, 0, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 1, 0, 1, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 2, 0, 2, 0, 2, 0, 2, 1, 0, 0, 1, 2, 0, 2, 2, 2, 0, 2, 2, 2, 2, 1, 0, 2, 1, 2, 2, 1, 2, 0, 2, 0, 2, 2, 2, 2, 2, 2, 1, 2, 1, 0, 2, 1, 1, 2, 2, 2, 2, 0, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 0, 1, 2, 0, 2, 2, 0, 1, 2, 2, 2, 1, 0, 1, 2, 1, 2, 1, 1, 1, 2, 1, 2, 1, 0, 0, 2, 0, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1]","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"n_folds = 5\nearly_stopping_rounds = 10\nsettings = settings = [4]\nnp.random.seed(47)\n\nsettings_best_ind = []\nfeatures_used = [features, features_count]\n\ndef train_trees():\n    preds_oof = np.zeros((len(X_train), len(features)))\n    preds_test = np.zeros((len(X_test), len(features)))\n    preds_train = np.zeros((len(X_train), len(features)))\n    preds_fake = np.zeros((len(X_fake), len(features)))\n\n    features_used_flatten = [var for sublist in features_used for var in sublist]\n    X_train_used = X_train[features_used_flatten]\n    X_test_used = X_test[features_used_flatten]\n    X_fake_used = X_fake[features_used_flatten]\n\n    for i in range(len(features)):\n        params['learning_rate'] = learning_rate_values[learning_rate_var[i]]\n        features_train = [feature_set[i] for feature_set in features_used] \n        print(f'Training on: {features_train}')\n        folds = StratifiedKFold(n_splits=n_folds, shuffle=True, random_state=np.random.randint(100000))\n        list_folds = list(folds.split(X_train_used.values, target_train.values))\n        preds_oof_temp = np.zeros((preds_oof.shape[0], len(settings)))\n        preds_test_temp = np.zeros((preds_test.shape[0], len(settings)))\n        preds_train_temp = np.zeros((preds_train.shape[0], len(settings)))\n        preds_fake_temp = np.zeros((preds_fake.shape[0], len(settings)))\n\n        scores = []\n        for j, setting in enumerate(settings):\n            # setting is used for hyperparameter tuning, here you can add sometinh like params['num_leaves'] = setting\n            print('\\nsetting: ', setting)\n            for k, (trn_idx, val_idx) in enumerate(list_folds):\n                print(\"Fold: {}\".format(k+1), end=\"\")\n                \n                # Prepare CatBoost Pool for training and validation data\n                trn_data = Pool(X_train_used.iloc[trn_idx][features_train], label=target_train.iloc[trn_idx])\n                val_data = Pool(X_train_used.iloc[val_idx][features_train], label=target_train.iloc[val_idx])\n\n                # CatBoost model initialization\n                clf = CatBoostClassifier(\n                    iterations=2000,\n                    learning_rate=params['learning_rate'],\n                    depth=params['depth'],  # Use num_leaves as depth\n                    l2_leaf_reg=params['l2_leaf_reg'],  # reg_alpha maps to l2_leaf_reg\n                    early_stopping_rounds=early_stopping_rounds,\n                    custom_metric=['AUC', 'Logloss'],\n                    verbose=0\n                )\n                \n                clf.fit(trn_data, eval_set=val_data, use_best_model=True)\n\n                prediction_val1 = clf.predict_proba(X_train_used.iloc[val_idx][features_train])[:, 1]\n                prediction_test1 = clf.predict_proba(X_test_used[features_train])[:, 1]\n                prediction_train1 = clf.predict_proba(X_train_used.iloc[trn_idx][features_train])[:, 1]\n                prediction_fake1 = clf.predict_proba(X_fake_used[features_train])[:, 1]\n\n                # Predictions\n                s1 = roc_auc_score(target_train.iloc[val_idx], prediction_val1)\n                s1_log = log_loss(target_train.iloc[val_idx], prediction_val1)\n                print(' - val AUC: {:<8.4f} - loss: {:<8.3f}'.format(s1, s1_log*1000), end='')\n\n                # Predictions Train\n                s1_train = roc_auc_score(target_train.iloc[trn_idx], prediction_train1)\n                s1_log_train = log_loss(target_train.iloc[trn_idx], prediction_train1)\n                print(' - train AUC: {:<8.4f} - loss: {:<8.3f}'.format(s1_train, s1_log_train*1000), end='')\n\n                print('')\n\n                preds_oof_temp[val_idx,j] += np.sqrt(prediction_val1 - prediction_val1.mean() + 0.1)\n                preds_test_temp[:,j] += np.sqrt(prediction_test1 - prediction_test1.mean() + 0.1) / n_folds\n                preds_train_temp[trn_idx,j] += np.sqrt(prediction_train1 - prediction_train1.mean() + 0.1) / (n_folds-1)\n                preds_fake_temp[:,j] += np.sqrt(prediction_fake1 - prediction_fake1.mean() + 0.1) / n_folds\n\n            score_setting = roc_auc_score(target_train, preds_oof_temp[:,j])\n            score_setting_log = 1000 * log_loss(target_train, np.exp(preds_oof_temp[:,j]))\n            scores.append(score_setting_log)\n            print(\"Score:  - val AUC: {:<8.4f} - loss: {:<8.3f}\".format(score_setting, score_setting_log), end='')\n\n            score_setting_train = roc_auc_score(target_train, preds_train_temp[:,j])\n            score_setting_log_train = 1000 * log_loss(target_train, np.exp(preds_train_temp[:,j]))\n            print(\" - train AUC: {:<8.4f} - loss: {:<8.3f}\".format(score_setting_train, score_setting_log_train))\n\n        best_ind = np.argmin(scores)\n        settings_best_ind.append(best_ind)\n        preds_oof[:,i] = preds_oof_temp[:,best_ind]\n        preds_test[:,i] = preds_test_temp[:,best_ind]\n        preds_train[:,i] = preds_train_temp[:,best_ind]\n        preds_fake[:,i] = preds_fake_temp[:,best_ind]\n\n        print('\\nbest setting: ', settings[best_ind])\n        preds_oof_cum = preds_oof[:,:i+1].mean(axis=1)\n        print(\"Cum CV val  : {:<8.4f} - loss: {:<8.3f}\".format(roc_auc_score(target_train, preds_oof_cum), 1000 * log_loss(target_train, np.exp(preds_oof_cum))))\n        preds_train_cum = preds_train[:,:i+1].mean(axis=1)\n        print(\"Cum CV train: {:<8.4f} - loss: {:<8.3f}\".format(roc_auc_score(target_train, preds_train_cum), 1000 * log_loss(target_train, np.exp(preds_train_cum))))\n        print('*****' * 10 + '\\n')\n        \n    return preds_oof, preds_test, preds_train, preds_fake\n\npreds_oof, preds_test, preds_train, preds_fake = train_trees()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 得到集成在train和val上的效果","metadata":{}},{"cell_type":"code","source":"preds_oof_cum = np.zeros(preds_oof.shape[0])\npreds_train_cum = np.zeros(preds_train.shape[0])\nfor i in range(len(features)):\n    preds_oof_cum += preds_oof[:,i]\n    preds_train_cum += preds_train[:,i]\n    print(\"var_{} Cum val: {:<8.5f}\".format(i,roc_auc_score(target_train, preds_oof_cum)), end=\"\")\n    print(\" - train: {:<8.5f}\".format(roc_auc_score(target_train, preds_train_cum)))","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 评估集成的效果","metadata":{}},{"cell_type":"code","source":"target_train","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"preds_train_cum = (preds_train_cum - preds_train_cum.min()) / (preds_train_cum.max() - preds_train_cum.min())\npreds_oof_cum=(preds_oof_cum - preds_oof_cum.min()) / (preds_oof_cum.max() - preds_oof_cum.min())","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# 1. 评估训练集\ntrain_auc = evaluate_binary_classifier(\n    y_true=target_train,  # 真实的训练标签\n    y_pred=(preds_train_cum > 0.5).astype(int),  # 二值化预测值（标签）\n    y_proba=preds_train_cum,  # 概率值\n    model_name=\"catboost_e\",  # 模型名称\n    title_suffix=\"(Training Set)\",  # 标题后缀\n    save_results=True\n)\n\n# 2. 评估验证集\nval_auc = evaluate_binary_classifier(\n    y_true=target_train,  # 真实的验证标签\n    y_pred=(preds_oof_cum > 0.5).astype(int),  # 二值化预测值（标签）\n    y_proba=preds_oof_cum,  # 概率值\n    model_name=\"catboost_e\",  # 模型名称\n    title_suffix=\"(Validation Set)\",  # 标题后缀\n    save_results=True\n)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 用集成模型测试test\n","metadata":{}},{"cell_type":"code","source":"preds_test_cum = np.zeros(preds_test.shape[0])\nfor i in range(len(features)):\n    preds_test_cum += preds_test[:,i]","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 非集成训练，没有预处理的情况下在public上0.89900","metadata":{"execution":{"iopub.status.busy":"2025-09-13T10:38:36.668472Z","iopub.execute_input":"2025-09-13T10:38:36.668799Z","iopub.status.idle":"2025-09-13T10:38:36.688705Z","shell.execute_reply.started":"2025-09-13T10:38:36.668773Z","shell.execute_reply":"2025-09-13T10:38:36.687683Z"}}},{"cell_type":"code","source":"def train_catboost_cv(\n    X, y, n_splits=5, X_test=None, cat_features=None,\n    cb_params=None, random_state=42, verbose=100,\n    train_idx_list=None, valid_idx_list=None\n):\n    \"\"\"\n    CatBoost 分层交叉验证（支持自定义折划分：train_idx_list/valid_idx_list）\n    返回：\n        models, fold_aucs, oof_pred\n    \"\"\"\n\n    # 将 y 对齐到 X 的索引，避免 label 索引与位置不一致导致赋值问题\n    y_series = pd.Series(np.asarray(y).ravel(), index=X.index)\n\n    # 默认参数{'score_function': 'Cosine', 'max_ctr_complexity': 1}\n    default_params = dict(\n        loss_function='Logloss',\n        eval_metric='AUC',\n        iterations=10000,#6000\n        learning_rate= 0.07115307337532982,#0.03\n        depth=2,#3\n        l2_leaf_reg= 2.8095454424629818,#7\n        subsample=0.7010569330956639,#0.8\n        bootstrap_type='Bernoulli',\n        random_strength=7.404421728251231,#\n        min_data_in_leaf=8,#\n        max_bin=390,#\n        grow_policy='Depthwise',#\n        leaf_estimation_iterations=3,#\n        max_ctr_complexity=1,#\n        score_function='Cosine',#\n        random_seed=random_state,\n        od_type='Iter',\n        od_wait=100,\n        verbose=verbose,\n        thread_count=-1,\n    )\n    if cb_params is not None:\n        default_params.update(cb_params)\n\n    # 如果未传入自定义索引列表，则回退到原本的 StratifiedKFold\n    if (train_idx_list is None) or (valid_idx_list is None):\n        skf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=random_state)\n        train_idx_list, valid_idx_list = [], []\n        for tr_idx, va_idx in skf.split(X, y_series.values):\n            # 用 X 的索引标签来存储，保持与 .loc 选择一致\n            train_idx_list.append(X.iloc[tr_idx].index)\n            valid_idx_list.append(X.iloc[va_idx].index)\n\n    total_folds = len(train_idx_list)\n\n    models = []\n    # 用 Series 保存 OOF，按标签赋值更安全\n    oof_pred_s = pd.Series(0.0, index=X.index)\n    fold_aucs = []\n\n    for fold in range(total_folds):\n        print(f\"\\n===== Fold {fold+1}/{total_folds} =====\")\n        tr_idx = train_idx_list[fold]\n        va_idx = valid_idx_list[fold]\n\n        # 按标签索引选择，更稳健（兼容非连续/自定义索引）\n        X_train, X_val = X.loc[tr_idx], X.loc[va_idx]\n        y_train, y_val = y_series.loc[tr_idx].values, y_series.loc[va_idx].values\n\n        # 每折 class_weights\n        pos = np.sum(y_train == 1)\n        neg = np.sum(y_train == 0)\n        class_weights = [1.0, float(neg) / float(pos)] if pos > 0 else [1.0, 1.0]\n\n        params_fold = default_params.copy()\n        params_fold['class_weights'] = params_fold.get('class_weights', class_weights)\n\n        train_pool = Pool(X_train, y_train, cat_features=cat_features)\n        val_pool   = Pool(X_val,   y_val,   cat_features=cat_features)\n\n        model = CatBoostClassifier(**params_fold)\n        model.fit(train_pool, eval_set=val_pool, use_best_model=True)\n\n        # ===== 训练集评估（可选）=====\n        y_pred_tr = model.predict(X_train)\n        y_proba_tr = model.predict_proba(X_train)[:, 1]\n        try:\n            metrics = evaluate_binary_classifier(\n                y_true=y_train,\n                y_pred=y_pred_tr,\n                y_proba=y_proba_tr,\n                model=model,\n                model_name=\"catboost\",\n                label_names=('Class 0','Class 1'),\n                title_suffix=f'(Train Set)_{fold+1}',\n                save_results=True\n            )\n        except NameError:\n            pass  # 若外部未提供 evaluate_binary_classifier，则跳过\n\n        # ===== 验证集评估 =====\n        y_pred_val = model.predict(X_val)\n        y_proba_val = model.predict_proba(X_val)[:, 1]\n\n        # OOF：按标签索引赋值，避免位置错乱\n        oof_pred_s.loc[va_idx] = y_proba_val\n\n        fold_auc = roc_auc_score(y_val, y_proba_val)\n        fold_aucs.append(fold_auc)\n\n        try:\n            metrics = evaluate_binary_classifier(\n                y_true=y_val,\n                y_pred=y_pred_val,\n                y_proba=y_proba_val,\n                model=model,\n                model_name=\"catboost\",\n                label_names=('Class 0','Class 1'),\n                title_suffix=f'(Validation Set)_{fold+1}',\n                save_results=True\n            )\n        except NameError:\n            pass\n\n        models.append(model)\n\n    return models, fold_aucs, oof_pred_s.values\n    \ncatboost_models, fold_aucs, oof_pred = train_catboost_cv(\n        X_train, target_train,\n        verbose=100\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T14:48:17.113672Z","iopub.execute_input":"2025-09-16T14:48:17.113986Z","iopub.status.idle":"2025-09-16T15:06:48.013845Z","shell.execute_reply.started":"2025-09-16T14:48:17.113964Z","shell.execute_reply":"2025-09-16T15:06:48.012796Z"}},"outputs":[{"name":"stdout","text":"\n===== Fold 1/5 =====\n0:\ttest: 0.5674819\tbest: 0.5674819 (0)\ttotal: 77.3ms\tremaining: 12m 52s\n100:\ttest: 0.8053574\tbest: 0.8053574 (100)\ttotal: 6.61s\tremaining: 10m 47s\n200:\ttest: 0.8385550\tbest: 0.8385550 (200)\ttotal: 12.9s\tremaining: 10m 29s\n300:\ttest: 0.8522728\tbest: 0.8522783 (298)\ttotal: 19.1s\tremaining: 10m 14s\n400:\ttest: 0.8618423\tbest: 0.8618423 (400)\ttotal: 25.4s\tremaining: 10m 7s\n500:\ttest: 0.8695049\tbest: 0.8695268 (499)\ttotal: 31.7s\tremaining: 10m 1s\n600:\ttest: 0.8749215\tbest: 0.8749215 (600)\ttotal: 38s\tremaining: 9m 53s\n700:\ttest: 0.8794317\tbest: 0.8794317 (700)\ttotal: 44.5s\tremaining: 9m 49s\n800:\ttest: 0.8827591\tbest: 0.8827591 (800)\ttotal: 50.9s\tremaining: 9m 45s\n900:\ttest: 0.8853361\tbest: 0.8853664 (898)\ttotal: 57.3s\tremaining: 9m 38s\n1000:\ttest: 0.8871005\tbest: 0.8871005 (1000)\ttotal: 1m 3s\tremaining: 9m 33s\n1100:\ttest: 0.8888278\tbest: 0.8888278 (1100)\ttotal: 1m 10s\tremaining: 9m 27s\n1200:\ttest: 0.8906258\tbest: 0.8906347 (1199)\ttotal: 1m 16s\tremaining: 9m 21s\n1300:\ttest: 0.8918876\tbest: 0.8918876 (1300)\ttotal: 1m 23s\tremaining: 9m 15s\n1400:\ttest: 0.8930868\tbest: 0.8930910 (1399)\ttotal: 1m 29s\tremaining: 9m 9s\n1500:\ttest: 0.8939494\tbest: 0.8939651 (1499)\ttotal: 1m 36s\tremaining: 9m 4s\n1600:\ttest: 0.8947406\tbest: 0.8947406 (1600)\ttotal: 1m 42s\tremaining: 8m 58s\n1700:\ttest: 0.8953887\tbest: 0.8953887 (1700)\ttotal: 1m 48s\tremaining: 8m 51s\n1800:\ttest: 0.8959594\tbest: 0.8959757 (1793)\ttotal: 1m 55s\tremaining: 8m 45s\n1900:\ttest: 0.8964369\tbest: 0.8964483 (1896)\ttotal: 2m 1s\tremaining: 8m 39s\n2000:\ttest: 0.8968570\tbest: 0.8968570 (2000)\ttotal: 2m 8s\tremaining: 8m 32s\n2100:\ttest: 0.8972504\tbest: 0.8972647 (2099)\ttotal: 2m 14s\tremaining: 8m 26s\n2200:\ttest: 0.8975479\tbest: 0.8975680 (2198)\ttotal: 2m 21s\tremaining: 8m 20s\n2300:\ttest: 0.8978398\tbest: 0.8978398 (2300)\ttotal: 2m 27s\tremaining: 8m 13s\n2400:\ttest: 0.8980307\tbest: 0.8980541 (2394)\ttotal: 2m 33s\tremaining: 8m 6s\n2500:\ttest: 0.8982303\tbest: 0.8982326 (2498)\ttotal: 2m 40s\tremaining: 8m\n2600:\ttest: 0.8983624\tbest: 0.8983630 (2580)\ttotal: 2m 46s\tremaining: 7m 53s\n2700:\ttest: 0.8984601\tbest: 0.8984806 (2683)\ttotal: 2m 52s\tremaining: 7m 47s\n2800:\ttest: 0.8986709\tbest: 0.8986777 (2799)\ttotal: 2m 59s\tremaining: 7m 40s\n2900:\ttest: 0.8988316\tbest: 0.8988338 (2897)\ttotal: 3m 5s\tremaining: 7m 34s\n3000:\ttest: 0.8989227\tbest: 0.8989344 (2985)\ttotal: 3m 12s\tremaining: 7m 28s\n3100:\ttest: 0.8990504\tbest: 0.8990745 (3095)\ttotal: 3m 18s\tremaining: 7m 21s\n3200:\ttest: 0.8991591\tbest: 0.8991642 (3199)\ttotal: 3m 24s\tremaining: 7m 15s\n3300:\ttest: 0.8991298\tbest: 0.8991823 (3236)\ttotal: 3m 31s\tremaining: 7m 8s\nStopped by overfitting detector  (100 iterations wait)\n\nbestTest = 0.8991822693\nbestIteration = 3236\n\nShrink model to first 3237 iterations.\n\n=== (Train Set)_1 ===\nAccuracy: 0.8577\nAUC: 0.9331\n\n分类报告:\n              precision    recall  f1-score   support\n\n           0       0.98      0.86      0.92    143921\n           1       0.40      0.86      0.55     16079\n\n    accuracy                           0.86    160000\n   macro avg       0.69      0.86      0.73    160000\nweighted avg       0.92      0.86      0.88    160000\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 600x400 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 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\n"},"metadata":{}},{"name":"stdout","text":"\n=== (Validation Set)_1 ===\nAccuracy: 0.8465\nAUC: 0.8992\n\n分类报告:\n              precision    recall  f1-score   support\n\n           0       0.97      0.85      0.91     35981\n           1       0.37      0.79      0.51      4019\n\n    accuracy                           0.85     40000\n   macro avg       0.67      0.82      0.71     40000\nweighted avg       0.91      0.85      0.87     40000\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 600x400 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 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\n"},"metadata":{}},{"name":"stdout","text":"\n===== Fold 2/5 =====\n0:\ttest: 0.5645855\tbest: 0.5645855 (0)\ttotal: 78.5ms\tremaining: 13m 5s\n100:\ttest: 0.8099808\tbest: 0.8099808 (100)\ttotal: 6.65s\tremaining: 10m 51s\n200:\ttest: 0.8413532\tbest: 0.8413532 (200)\ttotal: 12.9s\tremaining: 10m 29s\n300:\ttest: 0.8554392\tbest: 0.8555767 (299)\ttotal: 19.1s\tremaining: 10m 15s\n400:\ttest: 0.8656120\tbest: 0.8656120 (400)\ttotal: 25.4s\tremaining: 10m 6s\n500:\ttest: 0.8725907\tbest: 0.8727921 (496)\ttotal: 33.7s\tremaining: 10m 39s\n600:\ttest: 0.8777462\tbest: 0.8777462 (600)\ttotal: 40.2s\tremaining: 10m 28s\n700:\ttest: 0.8812204\tbest: 0.8812204 (700)\ttotal: 46.6s\tremaining: 10m 18s\n800:\ttest: 0.8844582\tbest: 0.8844582 (800)\ttotal: 53s\tremaining: 10m 8s\n900:\ttest: 0.8866929\tbest: 0.8866929 (900)\ttotal: 59.3s\tremaining: 9m 59s\n1000:\ttest: 0.8883319\tbest: 0.8883523 (999)\ttotal: 1m 5s\tremaining: 9m 50s\n1100:\ttest: 0.8899228\tbest: 0.8899306 (1099)\ttotal: 1m 12s\tremaining: 9m 43s\n1200:\ttest: 0.8913342\tbest: 0.8913680 (1197)\ttotal: 1m 18s\tremaining: 9m 35s\n1300:\ttest: 0.8924285\tbest: 0.8924285 (1300)\ttotal: 1m 24s\tremaining: 9m 27s\n1400:\ttest: 0.8934190\tbest: 0.8934190 (1400)\ttotal: 1m 31s\tremaining: 9m 21s\n1500:\ttest: 0.8941716\tbest: 0.8941716 (1500)\ttotal: 1m 37s\tremaining: 9m 13s\n1600:\ttest: 0.8949631\tbest: 0.8949846 (1597)\ttotal: 1m 44s\tremaining: 9m 6s\n1700:\ttest: 0.8957605\tbest: 0.8957739 (1699)\ttotal: 1m 50s\tremaining: 9m\n1800:\ttest: 0.8962051\tbest: 0.8962268 (1794)\ttotal: 1m 57s\tremaining: 8m 53s\n1900:\ttest: 0.8965827\tbest: 0.8966005 (1898)\ttotal: 2m 3s\tremaining: 8m 45s\n2000:\ttest: 0.8969605\tbest: 0.8969644 (1999)\ttotal: 2m 9s\tremaining: 8m 38s\n2100:\ttest: 0.8971830\tbest: 0.8971893 (2098)\ttotal: 2m 16s\tremaining: 8m 32s\n2200:\ttest: 0.8975305\tbest: 0.8975395 (2198)\ttotal: 2m 22s\tremaining: 8m 25s\n2300:\ttest: 0.8978489\tbest: 0.8978489 (2300)\ttotal: 2m 28s\tremaining: 8m 18s\n2400:\ttest: 0.8980604\tbest: 0.8980604 (2400)\ttotal: 2m 35s\tremaining: 8m 11s\n2500:\ttest: 0.8981717\tbest: 0.8981960 (2497)\ttotal: 2m 41s\tremaining: 8m 4s\n2600:\ttest: 0.8983830\tbest: 0.8983830 (2600)\ttotal: 2m 48s\tremaining: 7m 57s\n2700:\ttest: 0.8985496\tbest: 0.8985541 (2699)\ttotal: 2m 54s\tremaining: 7m 51s\n2800:\ttest: 0.8986709\tbest: 0.8986754 (2798)\ttotal: 3m\tremaining: 7m 44s\n2900:\ttest: 0.8987029\tbest: 0.8987514 (2879)\ttotal: 3m 7s\tremaining: 7m 38s\nStopped by overfitting detector  (100 iterations wait)\n\nbestTest = 0.8987513654\nbestIteration = 2879\n\nShrink model to first 2880 iterations.\n\n=== (Train Set)_2 ===\nAccuracy: 0.8558\nAUC: 0.9308\n\n分类报告:\n              precision    recall  f1-score   support\n\n           0       0.98      0.86      0.91    143921\n           1       0.40      0.85      0.54     16079\n\n    accuracy                           0.86    160000\n   macro avg       0.69      0.85      0.73    160000\nweighted avg       0.92      0.86      0.88    160000\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 600x400 with 1 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 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\n"},"metadata":{}},{"name":"stdout","text":"\n=== (Validation Set)_2 ===\nAccuracy: 0.8426\nAUC: 0.8988\n\n分类报告:\n              precision    recall  f1-score   support\n\n           0       0.97      0.85      0.91     35981\n           1       0.37      0.78      0.50      4019\n\n    accuracy                           0.84     40000\n   macro avg       0.67      0.82      0.70     40000\nweighted avg       0.91      0.84      0.87     40000\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 600x400 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 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\n"},"metadata":{}},{"name":"stdout","text":"\n===== Fold 3/5 =====\n0:\ttest: 0.5642382\tbest: 0.5642382 (0)\ttotal: 78.5ms\tremaining: 13m 4s\n100:\ttest: 0.7966112\tbest: 0.7966112 (100)\ttotal: 6.59s\tremaining: 10m 45s\n200:\ttest: 0.8257275\tbest: 0.8257275 (200)\ttotal: 13s\tremaining: 10m 33s\n300:\ttest: 0.8449767\tbest: 0.8449767 (300)\ttotal: 19.2s\tremaining: 10m 17s\n400:\ttest: 0.8548249\tbest: 0.8548249 (400)\ttotal: 25.5s\tremaining: 10m 10s\n500:\ttest: 0.8620660\tbest: 0.8620660 (500)\ttotal: 31.8s\tremaining: 10m 3s\n600:\ttest: 0.8677267\tbest: 0.8678225 (597)\ttotal: 38.3s\tremaining: 9m 58s\n700:\ttest: 0.8714012\tbest: 0.8714012 (700)\ttotal: 44.7s\tremaining: 9m 53s\n800:\ttest: 0.8751679\tbest: 0.8751679 (800)\ttotal: 51.2s\tremaining: 9m 47s\n900:\ttest: 0.8778895\tbest: 0.8778895 (900)\ttotal: 57.7s\tremaining: 9m 42s\n1000:\ttest: 0.8799900\tbest: 0.8799900 (1000)\ttotal: 1m 4s\tremaining: 9m 36s\n1100:\ttest: 0.8818161\tbest: 0.8818161 (1100)\ttotal: 1m 10s\tremaining: 9m 29s\n1200:\ttest: 0.8832644\tbest: 0.8832644 (1200)\ttotal: 1m 16s\tremaining: 9m 23s\n1300:\ttest: 0.8842460\tbest: 0.8842622 (1299)\ttotal: 1m 23s\tremaining: 9m 17s\n1400:\ttest: 0.8854483\tbest: 0.8854483 (1400)\ttotal: 1m 29s\tremaining: 9m 10s\n1500:\ttest: 0.8868492\tbest: 0.8868492 (1500)\ttotal: 1m 36s\tremaining: 9m 4s\n1600:\ttest: 0.8874424\tbest: 0.8874555 (1596)\ttotal: 1m 42s\tremaining: 8m 57s\n1700:\ttest: 0.8881857\tbest: 0.8881887 (1694)\ttotal: 1m 48s\tremaining: 8m 51s\n1800:\ttest: 0.8888253\tbest: 0.8888452 (1795)\ttotal: 1m 55s\tremaining: 8m 45s\n1900:\ttest: 0.8892121\tbest: 0.8892121 (1900)\ttotal: 2m 1s\tremaining: 8m 38s\n2000:\ttest: 0.8897181\tbest: 0.8897297 (1995)\ttotal: 2m 8s\tremaining: 8m 32s\n2100:\ttest: 0.8901254\tbest: 0.8901254 (2100)\ttotal: 2m 14s\tremaining: 8m 26s\n2200:\ttest: 0.8904057\tbest: 0.8904189 (2199)\ttotal: 2m 21s\tremaining: 8m 19s\n2300:\ttest: 0.8907898\tbest: 0.8908088 (2297)\ttotal: 2m 27s\tremaining: 8m 13s\n2400:\ttest: 0.8910090\tbest: 0.8910283 (2393)\ttotal: 2m 33s\tremaining: 8m 7s\n2500:\ttest: 0.8912624\tbest: 0.8912909 (2492)\ttotal: 2m 40s\tremaining: 8m\n2600:\ttest: 0.8916326\tbest: 0.8916434 (2599)\ttotal: 2m 46s\tremaining: 7m 54s\n2700:\ttest: 0.8918969\tbest: 0.8918969 (2700)\ttotal: 2m 53s\tremaining: 7m 47s\n2800:\ttest: 0.8920289\tbest: 0.8920667 (2782)\ttotal: 2m 59s\tremaining: 7m 41s\n2900:\ttest: 0.8921908\tbest: 0.8922014 (2890)\ttotal: 3m 5s\tremaining: 7m 34s\n3000:\ttest: 0.8924144\tbest: 0.8924195 (2999)\ttotal: 3m 12s\tremaining: 7m 28s\n3100:\ttest: 0.8925316\tbest: 0.8925603 (3061)\ttotal: 3m 18s\tremaining: 7m 21s\n3200:\ttest: 0.8926709\tbest: 0.8926739 (3199)\ttotal: 3m 24s\tremaining: 7m 15s\n3300:\ttest: 0.8927432\tbest: 0.8927678 (3287)\ttotal: 3m 31s\tremaining: 7m 8s\n3400:\ttest: 0.8927821\tbest: 0.8928187 (3355)\ttotal: 3m 37s\tremaining: 7m 2s\nStopped by overfitting detector  (100 iterations wait)\n\nbestTest = 0.8928187232\nbestIteration = 3355\n\nShrink model to first 3356 iterations.\n\n=== (Train Set)_3 ===\nAccuracy: 0.8600\nAUC: 0.9347\n\n分类报告:\n              precision    recall  f1-score   support\n\n           0       0.98      0.86      0.92    143922\n           1       0.41      0.86      0.55     16078\n\n    accuracy                           0.86    160000\n   macro avg       0.69      0.86      0.73    160000\nweighted avg       0.92      0.86      0.88    160000\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 600x400 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAjYAAAGJCAYAAACZwnkIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAABUiElEQVR4nO3de1yP9/8/8Me7w/tdSgc6eU8SEhEREubYhMbaGJnNYWGsnNoBcyhmszGHNcnsIHPYHIZZrCSnfWikREzGVA6pkN5vovP1+8O36+et84F3uzzut9v7Nl2v1/W6ntdV7f3oel+v65IJgiCAiIiISAJ0tF0AERERUV1hsCEiIiLJYLAhIiIiyWCwISIiIslgsCEiIiLJYLAhIiIiyWCwISIiIslgsCEiIiLJYLAhIiIiyWCwIcnp27cv2rdvr+0y6tSyZcvQpk0bFBcXP7dtHjlyBDKZDEeOHHlu26xLPj4+GDlypLbLoP/zPH+egoKCIJPJNJbJZDL4+/s/820DQFhYGGQyGVJSUp7L9kgTgw39J6WlpSEoKAgJCQnaLqVaalK3Wq3Gl19+idmzZ0NHRwfjx4+HTCar9DV+/Phnth81lZ+fj6+//hqdOnWCiYkJzMzM0K5dO0yePBlJSUnVHq+i4zl79mz8+uuvOHv2bLXH/e6779CnTx9YW1tDoVDA3t4eEyZMqPSNquQNtbJX3759q13Ts/b5559jz549VeqbkpKisT/6+vqwsLBAjx498Mknn+DatWtaqet5q8+1vchkfFYU/RedPn0aXbt2xYYNG0q9gfft2xd37tzB+fPntVNcBSqquzyrV69GYGAgMjIyYGBggJiYGPz7779ie3JyMhYuXIjJkyfj5ZdfFpe3bNkS7u7uNa61uLgY+fn5kMvl0NGpm7+Bhg4dij/++AOjR4+Gu7s7CgoKkJSUhPDwcHz66afVDmOVHU83Nzc4Ojrip59+qta477//Ph4+fAhnZ2eYm5sjOTkZ3333HYqKinD27Fkolcoy1zt37hzOnTsnfv3gwQNMnToVr7/+Ot544w1xubW1NV555ZVq1fSsGRsbY8SIEQgLC6u0b0pKCuzt7TF69GgMGTIExcXFuHfvHmJjY7Fr1y7IZDL88MMP8PHxEdep6c9TdeoqUVhYiMLCQhgYGIjLZDIZ/Pz8sGbNmiqPU9PaioqKUFBQAIVCUerMET17etougIgqtmHDBgwbNkz8n7S7u7tGYDl9+jQWLlwId3d3vP322+WOk5OTAyMjoypvV0dHR+ONobZiY2MRHh6Ozz77DJ988olG25o1a5CdnV1n2yoxcuRIBAYGYu3atTA2Nq7yemvXri21zNvbG126dMFPP/2EOXPmlLlehw4d0KFDB/HrO3fuYOrUqejQoUOF35uqqu738Fnr3Llzqf1KTU3FwIEDMW7cOLRt2xYdO3YEUPc/T2UpOT56enrQ09Pe25uuri50dXW1tv0XHT+Koufq5s2b8PX1hVKpFE/xT506Ffn5+QCArKwsfPjhh3B2doaxsTFMTEwwePBgjY8Tjhw5gq5duwIAJkyYIJ4Of/qvpri4OPTo0QOGhoawt7fHunXrStWTmZkJX19fWFtbw8DAAB07dsTGjRtL9cvJycEHH3wAW1tbKBQKODo64quvvsLTJzyjoqLQq1cvmJmZwdjYGI6OjuKbeFXrflJycjLOnTsHDw+Pyg/uE0o+4z969Cjef/99WFlZoWnTpgAev/G8//77cHR0hKGhIRo3bow333yz1McsZV0TUXL90t9//41+/fqhQYMGeOmll7Bs2bJKayo5y9SzZ89Sbbq6umjcuLHGsps3b+Ldd98VPw5q164dfvzxR436Kjuer7zyCnJychAVFVVpfZVp3rw5ANQ6gFX1+Ff0PQSAkJAQtGjRAoaGhujWrRv+/PNP9O3bt9THXHl5eQgMDESrVq2gUChga2uLjz/+GHl5eWIfmUyGnJwcbNy4sdYfZdrZ2SEsLAz5+fkaPxdl/TxdvnwZw4cPh42NDQwMDNC0aVP4+PhApVJVWlfJx35///033nrrLZibm6NXr14abWXZsmULHB0dYWBgAFdXVxw7dkyjffz48eL3+klPj1lRbeVdY7N27Vq0a9cOCoUCSqUSfn5+pX6eavM7Ro/xjA09N2lpaejWrRuys7MxefJktGnTBjdv3sTOnTvx8OFDyOVyXL16FXv27MGbb74Je3t7ZGRk4Ntvv0WfPn3w999/Q6lUom3btli8eHGpj1969OghbuvevXsYMmQIRo4cidGjR2P79u2YOnUq5HI53n33XQDAo0eP0LdvX1y5cgX+/v6wt7fHjh07MH78eGRnZ2PGjBkAAEEQMGzYMBw+fBi+vr5wcXFBZGQkPvroI9y8eROrVq0CAFy4cAGvvvoqOnTogMWLF0OhUODKlSs4fvw4AFSp7qedOHECwOO/jGvi/fffh6WlJRYuXIicnBwAj8+cnDhxAj4+PmjatClSUlIQGhqKvn374u+//0aDBg0qHPPevXsYNGgQ3njjDYwcORI7d+7E7Nmz4ezsjMGDB5e7np2dHYDHbyw9e/as8C/qjIwMdO/eXbzg09LSEn/88Qd8fX2hVqsxc+bMKh1PJycnGBoa4vjx43j99derfNxK3L17F0VFRbh27RoWL14MABgwYEC1x3lSdY9/Wd/D0NBQ+Pv74+WXX8asWbOQkpICb29vmJuba4Sf4uJiDBs2DP/73/8wefJktG3bFomJiVi1ahX++ecf8fqQTZs2YeLEiejWrRsmT54M4PFHmTXl7u6Oli1bVhgo8/Pz4enpiby8PEybNg02Nja4efMmwsPDkZ2dDVNT0yrV9eabb8LBwQGff/55qT80nnb06FFs27YN06dPh0KhwNq1azFo0CCcOnWq2hMOqnvMgoKCsGjRInh4eGDq1Km4dOkSQkNDERsbi+PHj0NfX1/sW9PfMfo/AtFzMnbsWEFHR0eIjY0t1VZcXCwIgiDk5uYKRUVFGm3JycmCQqEQFi9eLC6LjY0VAAgbNmwoNVafPn0EAMKKFSvEZXl5eYKLi4tgZWUl5OfnC4IgCKtXrxYACJs3bxb75efnC+7u7oKxsbGgVqsFQRCEPXv2CACEJUuWaGxnxIgRgkwmE65cuSIIgiCsWrVKACDcvn273GNQUd1lmT9/vgBAuH//frXG3LBhgwBA6NWrl1BYWKjR/+HDh6XGiImJEQAIP/30k7js8OHDAgDh8OHD4rKSY/tkv7y8PMHGxkYYPnx4hftSXFwsrm9tbS2MHj1aCAkJEVJTU0v19fX1FZo0aSLcuXNHY7mPj49gamoq7kNVjmfr1q2FwYMHV1hbeRQKhQBAACA0btxYCA4Ortb6t2/fFgAIgYGB4rKqHv/yvod5eXlC48aNha5duwoFBQXi8rCwMAGA0KdPH3HZpk2bBB0dHeHPP//U2N66desEAMLx48fFZUZGRsK4ceOqtF/JyckCAGH58uXl9nnttdcEAIJKpRIEofTP05kzZwQAwo4dOyrcVnl1BQYGCgCE0aNHl9v2pJLv4+nTp8VlqampgoGBgfD666+Ly8aNGyfY2dlVaczyaiv53iUnJwuCIAiZmZmCXC4XBg4cqPH/tzVr1ggAhB9//FFcVpvfMXqMH0XRc1FcXIw9e/Zg6NCh6NKlS6n2klO8CoVCvLCwqKgId+/eFT/SiY+Pr/L29PT08N5774lfy+VyvPfee8jMzERcXBwAYP/+/bCxscHo0aPFfvr6+pg+fToePHiAo0ePiv10dXUxffp0jW188MEHEAQBf/zxBwDAzMwMAPDbb7/V2bTsu3fvQk9Pr1rXhzxp0qRJpT7rNzQ0FP9dUFCAu3fvolWrVjAzM6vSMTY2Nta4rkIul6Nbt264evVqhevJZDJERkZiyZIlMDc3x88//ww/Pz/Y2dlh1KhR4il5QRDw66+/YujQoRAEAXfu3BFfnp6eUKlU1fpZMDc3x507d6rc/0l//PEH9u/fjxUrVqBZs2biGZPaqO7xf/p7ePr0ady9exeTJk3SOOs1ZswYmJuba6y7Y8cOtG3bFm3atNE4jv379wcAHD58uNb7U56Sn9n79++X2W5qagoAiIyMxMOHD2u8nSlTplS5r7u7O1xdXcWvmzVrhtdeew2RkZEoKiqqcQ2VOXjwIPLz8zFz5kyNC6cnTZoEExMT7Nu3T6N/TX/H6DEGG3oubt++DbVaXenp3uLiYqxatQoODg5QKBSwsLCApaUlzp07J37uXhVKpbLURZatW7cGAPFz79TUVDg4OJSaodG2bVuxveS/SqUSDRs2rLDfqFGj0LNnT0ycOBHW1tbw8fHB9u3bn+u9Z55mb29fatmjR4+wcOFC8XqhkmOcnZ1dpWPctGnTUtcvmJub4969e5Wuq1AoMG/ePFy8eBFpaWn4+eef0b17d2zfvl28x8jt27eRnZ2N9evXw9LSUuM1YcIEAI+vjaoqQRBqPDOlX79+GDx4MAICArBjxw4sWrSo1rNqqnv8n/4elvy8tWrVSmO5np5eqWtDLl++jAsXLpQ6jiW/C9U5jtX14MEDACj1e1PC3t4eAQEB+P7772FhYQFPT0+EhIRU6/e8ZJyqcnBwKLWsdevWePjwIW7fvl2t7VZHyffM0dFRY7lcLkeLFi3E9hK1+R0jXmND9cznn3+OBQsW4N1338Wnn36KRo0aQUdHBzNnztRqQKgKQ0NDHDt2DIcPH8a+ffsQERGBbdu2oX///jhw4ECNZkk0btwYhYWFuH//frlvEJXV9LRp06Zhw4YNmDlzJtzd3WFqagqZTAYfH58qHePy9kOo5p0jmjRpAh8fHwwfPhzt2rXD9u3bERYWJtbw9ttvY9y4cWWu++TMo8rcu3evzDe06mrZsiU6deqELVu21OpGb9U9/mV9D6uquLgYzs7OWLlyZZnttra2NR67MufPn4eVlRVMTEzK7bNixQqMHz8ev/32Gw4cOIDp06dj6dKl+OuvvzSuFapIbY5PWcoLwc/yjM7T6up37EXFYEPPhaWlJUxMTCq9t8zOnTvRr18//PDDDxrLs7OzYWFhIX5d2V/gaWlppabG/vPPPwD+/+wWOzs7nDt3DsXFxRpnbUpuFFdysaudnR0OHjxYKlw83Q94PKV1wIABGDBgAFauXInPP/8c8+bNw+HDh+Hh4VHtMwdt2rQB8Hh2VHXezCuyc+dOjBs3DitWrBCX5ebmPpPp1lWhr6+PDh064PLly7hz5w4sLS3RsGFDFBUVVTobrLLjWVhYiOvXr2PYsGF1UuujR480ZhPVRG2Pf8nP25UrV9CvXz9xeWFhIVJSUjR+Tlq2bImzZ89iwIABlR6rurzfSsm9lqoyxd3Z2RnOzs6YP38+Tpw4gZ49e2LdunVYsmRJndd1+fLlUsv++ecfNGjQAJaWlgAenxkp63vx9FmV6tRW8j27dOkSWrRoIS7Pz89HcnJytWc9UsX4URQ9Fzo6OvD29sbvv/+O06dPl2ov+UtEV1e31F8lO3bswM2bNzWWlQSW8t4MCgsL8e2334pf5+fn49tvv4WlpaX4GfuQIUOQnp6Obdu2aaz3zTffwNjYGH369BH7FRUVlfoIYtWqVZDJZOIshaysrFJ1uLi4AID4ZlhZ3U8ruV9NWcespso6xt98880z/4v08uXLZd6RNjs7GzExMTA3N4elpSV0dXUxfPhw/Prrr2UG4Sc/MqjseP7999/Izc2tcObZ0woLC8s85X/q1CkkJiaWeY1YddT2+Hfp0gWNGzfGd999h8LCQnH5li1bStU9cuRI3Lx5E999912pcR49eqRxzZCRkVGdhNvU1FSMHz8ecrkcH330Ubn91Gq1Rv3A45Cjo6OjER7rqi7gceB68jqm69ev47fffsPAgQPFsyQtW7aESqXSuNHirVu3sHv37lLjVbU2Dw8PyOVyBAcHa3zvf/jhB6hUKnh5edVir+hpPGNDz83nn3+OAwcOoE+fPuLU01u3bmHHjh343//+BzMzM7z66qtYvHgxJkyYgB49eiAxMRFbtmzR+CsHePw/HzMzM6xbtw4NGzaEkZER3NzcxM/blUolvvzyS6SkpKB169bYtm0bEhISsH79enFa5eTJk/Htt99i/PjxiIuLQ/PmzbFz504cP34cq1evFs/ODB06FP369cO8efOQkpKCjh074sCBA/jtt98wc+ZMcYrn4sWLcezYMXh5ecHOzg6ZmZlYu3YtmjZtKt5fo7K6n9aiRQu0b98eBw8eFKep19arr76KTZs2wdTUFE5OToiJicHBgwdL3Uemrp09exZvvfUWBg8ejJdffhmNGjXCzZs3sXHjRqSlpWH16tXim8sXX3yBw4cPw83NDZMmTYKTkxOysrIQHx+PgwcPiiGysuMZFRWFBg0aVOsuvw8ePICtrS1GjRqFdu3awcjICImJidiwYQNMTU2xYMGCWh2H2h5/uVyOoKAgTJs2Df3798fIkSORkpKCsLAwtGzZUuMswjvvvIPt27djypQpOHz4MHr27ImioiIkJSVh+/btiIyMFIOaq6srDh48iJUrV0KpVMLe3h5ubm4V1hIfH4/NmzejuLgY2dnZiI2Nxa+//gqZTIZNmzZVeJbx0KFD8Pf3x5tvvonWrVujsLAQmzZtEoNtiZrUVZ727dvD09NTY7o3ACxatEjs4+Pjg9mzZ+P111/H9OnT8fDhQ4SGhqJ169alLu6uam2WlpaYO3cuFi1ahEGDBmHYsGG4dOkS1q5di65du9bJzRvpCVqajUUvqNTUVGHs2LGCpaWloFAohBYtWgh+fn5CXl6eIAiPp3t/8MEHQpMmTQRDQ0OhZ8+eQkxMjNCnTx+NaayCIAi//fab4OTkJOjp6WlM+e3Tp4/Qrl074fTp04K7u7tgYGAg2NnZCWvWrClVT0ZGhjBhwgTBwsJCkMvlgrOzc5lTh+/fvy/MmjVLUCqVgr6+vuDg4CAsX75cnKYuCIIQHR0tvPbaa4JSqRTkcrmgVCqF0aNHC//880+V6i7PypUrBWNj4zKnCQtCxdO9y5paf+/ePXGfjY2NBU9PTyEpKUmws7PTmLpa3nTvdu3alRqzvCmyT8rIyBC++OILoU+fPkKTJk0EPT09wdzcXOjfv7+wc+fOMvv7+fkJtra2gr6+vmBjYyMMGDBAWL9+vUa/io6nm5ub8Pbbb1dY19Py8vKEGTNmCB06dBBMTEwEfX19wc7OTvD19RWn71ZVWdO9q3r8K/oeCoIgBAcHC3Z2doJCoRC6desmHD9+XHB1dRUGDRqk0S8/P1/48ssvhXbt2gkKhUIwNzcXXF1dhUWLFolTsQVBEJKSkoTevXsLhoaGAoAKp36XTPcueenp6QmNGjUS3NzchLlz55Y5hf/pn6erV68K7777rtCyZUvBwMBAaNSokdCvXz/h4MGDGuuVV1fJ9Ouybq9Q3nRvPz8/YfPmzYKDg4OgUCiETp06afx8lzhw4IDQvn17QS6XC46OjsLmzZvLHLO82p6e7l1izZo1Qps2bQR9fX3B2tpamDp1qnDv3j2NPrX5HaPH+KwoonpOpVKhRYsWWLZsGXx9fbVdzn9GQkICOnfujPj4ePEjQSkrLi6GpaUl3njjjTI/eiJ6UfAaG6J6ztTUFB9//DGWL19e72eG1SdffPEFRowYIclQk5ubW+o6nZ9++glZWVn18snhRM8Tz9gQ0Qvj9u3bFV6kK5fL0ahRo+dYUc0cOXIEs2bNwptvvonGjRsjPj4eP/zwA9q2bYu4uDjI5XJtl0ikNbx4mIheGF27di1z2m6JPn36aDyksb5q3rw5bG1tERwcjKysLDRq1Ahjx47FF198wVBDLzyesSGiF8bx48fx6NGjctvNzc01brlPRP89DDZEREQkGbx4mIiIiCSD19g8R8XFxUhLS0PDhg3r9DbhREREUicIAu7fvw+lUlnq4cVPYrB5jtLS0p7pQ+eIiIik7vr16xU+JJXB5jkquUX/9evXK3ziLREREWlSq9WwtbXVeBhxWRhsnqOSj59MTEwYbIiIiGqgsks5ePEwERERSQaDDVEN5efno1WrVjhx4oS2S3lmIiIi4OLiwkc5ENF/BoMN1bljx45h6NChUCqVkMlk2LNnj0Z7QUEBZs+eDWdnZxgZGUGpVGLs2LFIS0srNda+ffvg5uYGQ0NDmJubw9vbW6P92rVr8PLyQoMGDWBlZYWPPvoIhYWFZdZ1/Phx6OnplXp2UPPmzSGTyUq9/Pz8KtzPdevWwd7eHj169ADw+Db3ZY0jk8kQGxsrrhcZGYnu3bujYcOGsLS0xPDhw5GSklLudupq3DNnzqBTp04wNjbG0KFDkZWVJbYVFhbC1dUVp06d0tj2oEGDoK+vjy1btlR4LIiI6gsGG6pzOTk56NixI0JCQspsf/jwIeLj47FgwQLEx8dj165duHTpEoYNG6bR79dff8U777yDCRMm4OzZszh+/Djeeustsb2oqAheXl7Iz8/HiRMnsHHjRoSFhWHhwoWltpmdnY2xY8diwIABpdpiY2Nx69Yt8RUVFQUAePPNN8vdR0EQsGbNGo2nbffo0UNjnFu3bmHixImwt7dHly5dAADJycl47bXX0L9/fyQkJCAyMhJ37tzBG2+8Ue626mrciRMnon///oiPj4dKpcLnn38utq1YsQI9e/ZEt27dSm1//PjxCA4OLrc+IqJ6RaDnRqVSCQAElUql7VKeGwDC7t27K+136tQpAYCQmpoqCIIgFBQUCC+99JLw/fffl7vO/v37BR0dHSE9PV1cFhoaKpiYmAh5eXkafUeNGiXMnz9fCAwMFDp27FhhLTNmzBBatmwpFBcXl9snNjZW0NHREdRqdbl98vPzBUtLS2Hx4sXish07dgh6enpCUVGRuGzv3r2CTCYT8vPzK6yrtuMaGhoKFy9eFARBENauXSsMGTJEEARB+PfffwUHB4dy9yU1NVUAIFy5cqVK9RERPQtVfQ/lGRuqF1QqFWQyGczMzAAA8fHxuHnzJnR0dNCpUyc0adIEgwcPxvnz58V1YmJi4OzsDGtra3GZp6cn1Go1Lly4IC7bsGEDrl69isDAwErryM/Px+bNm/Huu+9WeOX9n3/+idatW1c47XDv3r24e/cuJkyYIC5zdXWFjo4ONmzYgKKiIqhUKmzatAkeHh7Q19evtL7ajNuxY0dERUWhsLAQ0dHR6NChAwBgypQpWLZsWbn70qxZM1hbW+PPP/+sUn1ERNrEYENal5ubi9mzZ2P06NHiNPirV68CAIKCgjB//nyEh4fD3Nwcffv2Fa8NSU9P1wg1AMSv09PTAQCXL1/GnDlzsHnzZujpVX53gz179iA7Oxvjx4+vsF9qaiqUSmWFfX744Qd4enpq3EjK3t4eBw4cwCeffAKFQgEzMzPcuHED27dvr7S22o77/fffY+fOnWjZsiXkcjnmzp2LTZs2oUGDBujatSs8PT3RqlUrzJ8/v9Q2lUplhU/FJiKqLxhsSKsKCgowcuRICIKA0NBQcXnJLJx58+Zh+PDhcHV1xYYNGyCTybBjx44qjV1UVIS33noLixYtQuvWrau0zg8//IDBgwdXGloePXoEAwODcttv3LiByMhIjWtwgMeBa9KkSRg3bhxiY2Nx9OhRyOVyjBgxAkIVnkdbm3HbtWuHo0ePIjU1FVu3bkVBQQECAwOxZs0aTJs2DT169MDZs2exa9cu/P777xrjGxoa4uHDh5XWR0SkbbxBH2lNSahJTU3FoUOHNG5a2KRJEwCAk5OTuEyhUKBFixa4du0aAMDGxqbULJ6MjAyx7f79+zh9+jTOnDkDf39/AI8DkyAI0NPTw4EDB9C/f39x3dTUVBw8eBC7du2qtHYLCwskJiaW275hwwY0bty41AXRISEhMDU1xbJly8Rlmzdvhq2tLU6ePInu3btXuN26HDcgIAAzZ85E06ZNceTIESxZsgRGRkbw8vLCkSNHMHToULFvVlYWLC0tK6yNiKg+4Bkb0oqSUHP58mUcPHgQjRs31mh3dXWFQqHApUuXNNZJSUmBnZ0dAMDd3R2JiYnIzMwU+0RFRcHExAROTk4wMTFBYmIiEhISxNeUKVPg6OiIhIQEuLm5aWxzw4YNsLKygpeXV6X1d+rUCUlJSWWeZREEARs2bMDYsWNLXTfz8OHDUg9v09XVBYBK7xVTl+NGR0fj4sWLYuArKipCQUEBgMfHuaioSOybm5uLf//9F506daqwPiKieuFZX8VM/9+LMivq/v37wpkzZ4QzZ84IAISVK1cKZ86cEWc85efnC8OGDROaNm0qJCQkCLdu3RJfT85mmjFjhvDSSy8JkZGRQlJSkuDr6ytYWVkJWVlZgiAIQmFhodC+fXth4MCBQkJCghARESFYWloKc+fOLbe28mZFFRUVCc2aNRNmz55dpX28c+eOoK+vLyQmJpZqO3jwoABAnIH0pOjoaEEmkwmLFi0S/vnnHyEuLk7w9PQU7OzshIcPHwqCIAgnT54UHB0dhRs3btTpuCUePXoktGnTRjhz5oy4bPDgwcKkSZOEhIQEoWnTpsL27dvFtsOHDwvGxsZCTk5OlY4NEdGzUNX3UJkgVOGDfaoTarUapqamUKlUz+RZUdN+PFLnY9bEjaQE7Fk+q9TyNj084eE7B+o76fhp9ugy1/X+aBWatnEBABQVFiLm1+9w6a8oFObnwaZFW/Ty8UPjl+zF/uo76Ti6eTVuXkqAntwAbXp4oseIydD5v7MVTzv5WxiSz/wPPkHfayy/dj4We1d9jDGf/QRzm6o9gT1i3SKYWCrRY/gkjeWR6z/F/bsZGDF3TZnr/XPyEM5E/ILsjOvQkxvApqUTeox4D+ZNmgH4/8dv7Jc/w8TCps7GLXHi1+9QXFiIXqOmisuyM27iwHdLkJ1+Ha3dBqDPmBmQ/d8ZoMM/rQAgQ7+xAVU6Ls/aN+/21XYJRKQFVX0PZbB5jl6UYPOiuHP9X/y28iO8s3QL5AaG2i7nmXh0X4XN88Zi1IJ1MLFsou1yADDYEL2oqvoeymtsiGrIwrYlegyfDPWdW9ou5ZlR301H37dn1JtQQ0RUGc6KIqqFtr0GabuEZ8q6uSOsmztquwwioirjGRsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDK0Gm2PHjmHo0KFQKpWQyWTYs2eP2FZQUIDZs2fD2dkZRkZGUCqVGDt2LNLS0jTGyMrKwpgxY2BiYgIzMzP4+vriwYMHGn3OnTuHl19+GQYGBrC1tdV4AnKJHTt2oE2bNjAwMICzszP279+v0S4IAhYuXIgmTZrA0NAQHh4euHz5ct0dDCIiIqo1rQabnJwcdOzYESEhIaXaHj58iPj4eCxYsADx8fHYtWsXLl26hGHDhmn0GzNmDC5cuICoqCiEh4fj2LFjmDx5stiuVqsxcOBA2NnZIS4uDsuXL0dQUBDWr18v9jlx4gRGjx4NX19fnDlzBt7e3vD29sb58+fFPsuWLUNwcDDWrVuHkydPwsjICJ6ensjNzX0GR4aIiIhqot48K0omk2H37t3w9vYut09sbCy6deuG1NRUNGvWDBcvXoSTkxNiY2PRpUsXAEBERASGDBmCGzduQKlUIjQ0FPPmzUN6ejrkcjkAYM6cOdizZw+SkpIAAKNGjUJOTg7Cw8PFbXXv3h0uLi5Yt24dBEGAUqnEBx98gA8//BAAoFKpYG1tjbCwMPj4+JRZb15eHvLy8sSv1Wo1bG1t+awoolrgs6KIXkySfFaUSqWCTCaDmZkZACAmJgZmZmZiqAEADw8P6Ojo4OTJk2Kf3r17i6EGADw9PXHp0iXcu3dP7OPh4aGxLU9PT8TExAAAkpOTkZ6ertHH1NQUbm5uYp+yLF26FKampuLL1rZqT40mIiKimvnPBJvc3FzMnj0bo0ePFpNaeno6rKysNPrp6emhUaNGSE9PF/tYW1tr9Cn5urI+T7Y/uV5Zfcoyd+5cqFQq8XX9+vVq7TMRERFVz3/iIZgFBQUYOXIkBEFAaGiotsupMoVCAYVCoe0yiIiIXhj1/oxNSahJTU1FVFSUxudqNjY2yMzM1OhfWFiIrKws2NjYiH0yMjI0+pR8XVmfJ9ufXK+sPkRERKR99TrYlISay5cv4+DBg2jcuLFGu7u7O7KzsxEXFycuO3ToEIqLi+Hm5ib2OXbsGAoKCsQ+UVFRcHR0hLm5udgnOjpaY+yoqCi4u7sDAOzt7WFjY6PRR61W4+TJk2IfIiIi0j6tBpsHDx4gISEBCQkJAB5fpJuQkIBr166hoKAAI0aMwOnTp7FlyxYUFRUhPT0d6enpyM/PBwC0bdsWgwYNwqRJk3Dq1CkcP34c/v7+8PHxgVKpBAC89dZbkMvl8PX1xYULF7Bt2zZ8/fXXCAgIEOuYMWMGIiIisGLFCiQlJSEoKAinT5+Gv78/gMcztmbOnIklS5Zg7969SExMxNixY6FUKiucxUVERETPl1anex85cgT9+vUrtXzcuHEICgqCvb19mesdPnwYffv2BfD4Bn3+/v74/fffoaOjg+HDhyM4OBjGxsZi/3PnzsHPzw+xsbGwsLDAtGnTMHv2bI0xd+zYgfnz5yMlJQUODg5YtmwZhgwZIrYLgoDAwECsX78e2dnZ6NWrF9auXYvWrVtXeX+rOlWtpjjdm14EnO5N9GKq6ntovbmPzYuAwYao9hhsiF5MkryPDREREVFFGGyIiIhIMhhsiIiISDIYbIiIiEgyGGyIiIhIMhhsiIiISDIYbIiIiEgyGGyIiIhIMhhsiIiISDIYbIiIiEgyGGyIiIhIMhhsiIiISDIYbIiIiEgyGGyIiIhIMhhsiIiISDIYbIiIiEgyGGyIiIhIMhhsiIiISDIYbIiIiEgyGGyIiIhIMhhsiIiISDIYbIiIiEgyGGyIiIhIMhhsiIiISDIYbIiIiEgyGGyIiIhIMhhsiIiISDIYbIiIiEgyGGyIiIhIMhhsiIiISDIYbIiIiEgyGGyIiIhIMhhsiIiISDIYbIiIiEgyGGyIiIhIMrQabI4dO4ahQ4dCqVRCJpNhz549Gu2CIGDhwoVo0qQJDA0N4eHhgcuXL2v0ycrKwpgxY2BiYgIzMzP4+vriwYMHGn3OnTuHl19+GQYGBrC1tcWyZctK1bJjxw60adMGBgYGcHZ2xv79+6tdCxEREWmXVoNNTk4OOnbsiJCQkDLbly1bhuDgYKxbtw4nT56EkZERPD09kZubK/YZM2YMLly4gKioKISHh+PYsWOYPHmy2K5WqzFw4EDY2dkhLi4Oy5cvR1BQENavXy/2OXHiBEaPHg1fX1+cOXMG3t7e8Pb2xvnz56tVCxEREWmXTBAEQdtFAIBMJsPu3bvh7e0N4PEZEqVSiQ8++AAffvghAEClUsHa2hphYWHw8fHBxYsX4eTkhNjYWHTp0gUAEBERgSFDhuDGjRtQKpUIDQ3FvHnzkJ6eDrlcDgCYM2cO9uzZg6SkJADAqFGjkJOTg/DwcLGe7t27w8XFBevWratSLVWhVqthamoKlUoFExOTOjluT5r245E6H5Oovvnm3b7aLoGItKCq76H19hqb5ORkpKenw8PDQ1xmamoKNzc3xMTEAABiYmJgZmYmhhoA8PDwgI6ODk6ePCn26d27txhqAMDT0xOXLl3CvXv3xD5PbqekT8l2qlJLWfLy8qBWqzVeRERE9OzU22CTnp4OALC2ttZYbm1tLbalp6fDyspKo11PTw+NGjXS6FPWGE9uo7w+T7ZXVktZli5dClNTU/Fla2tbyV4TERFRbdTbYCMFc+fOhUqlEl/Xr1/XdklERESSVm+DjY2NDQAgIyNDY3lGRobYZmNjg8zMTI32wsJCZGVlafQpa4wnt1FenyfbK6ulLAqFAiYmJhovIiIienbqbbCxt7eHjY0NoqOjxWVqtRonT56Eu7s7AMDd3R3Z2dmIi4sT+xw6dAjFxcVwc3MT+xw7dgwFBQVin6ioKDg6OsLc3Fzs8+R2SvqUbKcqtRAREZH2aTXYPHjwAAkJCUhISADw+CLdhIQEXLt2DTKZDDNnzsSSJUuwd+9eJCYmYuzYsVAqleLMqbZt22LQoEGYNGkSTp06hePHj8Pf3x8+Pj5QKpUAgLfeegtyuRy+vr64cOECtm3bhq+//hoBAQFiHTNmzEBERARWrFiBpKQkBAUF4fTp0/D39weAKtVCRERE2qenzY2fPn0a/fr1E78uCRvjxo1DWFgYPv74Y+Tk5GDy5MnIzs5Gr169EBERAQMDA3GdLVu2wN/fHwMGDICOjg6GDx+O4OBgsd3U1BQHDhyAn58fXF1dYWFhgYULF2rc66ZHjx7YunUr5s+fj08++QQODg7Ys2cP2rdvL/apSi1ERESkXfXmPjYvAt7Hhqj2eB8bohfTf/4+NkRERETVxWBDREREksFgQ0RERJLBYENERESSwWBDREREksFgQ0RERJLBYENERESSwWBDREREksFgQ0RERJLBYENERESSwWBDREREksFgQ0RERJLBYENERESSwWBDREREksFgQ0RERJLBYENERESSwWBDREREksFgQ0RERJLBYENERESSwWBDREREksFgQ0RERJLBYENERESSwWBDREREksFgQ0RERJLBYENERESSwWBDREREksFgQ0RERJLBYENERESSwWBDREREksFgQ0RERJLBYENERESSwWBDREREksFgQ0RERJLBYENERESSwWBDREREksFgQ0RERJJRr4NNUVERFixYAHt7exgaGqJly5b49NNPIQiC2EcQBCxcuBBNmjSBoaEhPDw8cPnyZY1xsrKyMGbMGJiYmMDMzAy+vr548OCBRp9z587h5ZdfhoGBAWxtbbFs2bJS9ezYsQNt2rSBgYEBnJ2dsX///mez40RERFQj9TrYfPnllwgNDcWaNWtw8eJFfPnll1i2bBm++eYbsc+yZcsQHByMdevW4eTJkzAyMoKnpydyc3PFPmPGjMGFCxcQFRWF8PBwHDt2DJMnTxbb1Wo1Bg4cCDs7O8TFxWH58uUICgrC+vXrxT4nTpzA6NGj4evrizNnzsDb2xve3t44f/788zkYREREVCmZ8OTpj3rm1VdfhbW1NX744Qdx2fDhw2FoaIjNmzdDEAQolUp88MEH+PDDDwEAKpUK1tbWCAsLg4+PDy5evAgnJyfExsaiS5cuAICIiAgMGTIEN27cgFKpRGhoKObNm4f09HTI5XIAwJw5c7Bnzx4kJSUBAEaNGoWcnByEh4eLtXTv3h0uLi5Yt25dlfZHrVbD1NQUKpUKJiYmdXKMnjTtxyN1PiZRffPNu321XQIRaUFV30Pr9RmbHj16IDo6Gv/88w8A4OzZs/jf//6HwYMHAwCSk5ORnp4ODw8PcR1TU1O4ubkhJiYGABATEwMzMzMx1ACAh4cHdHR0cPLkSbFP7969xVADAJ6enrh06RLu3bsn9nlyOyV9SrZTlry8PKjVao0XERERPTt62i6gInPmzIFarUabNm2gq6uLoqIifPbZZxgzZgwAID09HQBgbW2tsZ61tbXYlp6eDisrK412PT09NGrUSKOPvb19qTFK2szNzZGenl7hdsqydOlSLFq0qLq7TURERDVUr8/YbN++HVu2bMHWrVsRHx+PjRs34quvvsLGjRu1XVqVzJ07FyqVSnxdv35d2yURERFJWr0+Y/PRRx9hzpw58PHxAQA4OzsjNTUVS5cuxbhx42BjYwMAyMjIQJMmTcT1MjIy4OLiAgCwsbFBZmamxriFhYXIysoS17exsUFGRoZGn5KvK+tT0l4WhUIBhUJR3d0mIiKiGqrXZ2wePnwIHR3NEnV1dVFcXAwAsLe3h42NDaKjo8V2tVqNkydPwt3dHQDg7u6O7OxsxMXFiX0OHTqE4uJiuLm5iX2OHTuGgoICsU9UVBQcHR1hbm4u9nlyOyV9SrZDRERE2levg83QoUPx2WefYd++fUhJScHu3buxcuVKvP766wAAmUyGmTNnYsmSJdi7dy8SExMxduxYKJVKeHt7AwDatm2LQYMGYdKkSTh16hSOHz8Of39/+Pj4QKlUAgDeeustyOVy+Pr64sKFC9i2bRu+/vprBAQEiLXMmDEDERERWLFiBZKSkhAUFITTp0/D39//uR8XIiIiKlu9/ijqm2++wYIFC/D+++8jMzMTSqUS7733HhYuXCj2+fjjj5GTk4PJkycjOzsbvXr1QkREBAwMDMQ+W7Zsgb+/PwYMGAAdHR0MHz4cwcHBYrupqSkOHDgAPz8/uLq6wsLCAgsXLtS4102PHj2wdetWzJ8/H5988gkcHBywZ88etG/f/vkcDCIiIqpUvb6PjdTwPjZEtcf72BC9mCRxHxsiIiKi6mCwISIiIslgsCEiIiLJYLAhIiIiyWCwISIiIslgsCEiIiLJYLAhIiIiyahRsGnRogXu3r1banl2djZatGhR66KIiIiIaqJGwSYlJQVFRUWllufl5eHmzZu1LoqIiIioJqr1SIW9e/eK/46MjISpqan4dVFREaKjo9G8efM6K46IiIioOqoVbEoeLCmTyTBu3DiNNn19fTRv3hwrVqyos+KIiIiIqqNawaa4uBgAYG9vj9jYWFhYWDyTooiIiIhqokZP905OTq7rOoiIiIhqrUbBBgCio6MRHR2NzMxM8UxOiR9//LHWhRERERFVV42CzaJFi7B48WJ06dIFTZo0gUwmq+u6iIiIiKqtRsFm3bp1CAsLwzvvvFPX9RARERHVWI3uY5Ofn48ePXrUdS1EREREtVKjYDNx4kRs3bq1rmshIiIiqpUafRSVm5uL9evX4+DBg+jQoQP09fU12leuXFknxRERERFVR42Czblz5+Di4gIAOH/+vEYbLyQmIiIibalRsDl8+HBd10FERERUazW6xoaIiIioPqrRGZt+/fpV+JHToUOHalwQERERUU3VKNiUXF9ToqCgAAkJCTh//nyph2MSERERPS81CjarVq0qc3lQUBAePHhQq4KIiIiIaqpOr7F5++23+ZwoIiIi0po6DTYxMTEwMDCoyyGJiIiIqqxGH0W98cYbGl8LgoBbt27h9OnTWLBgQZ0URkRERFRdNQo2pqamGl/r6OjA0dERixcvxsCBA+ukMCIiIqLqqlGw2bBhQ13XQURERFRrNQo2JeLi4nDx4kUAQLt27dCpU6c6KYqIiIioJmoUbDIzM+Hj44MjR47AzMwMAJCdnY1+/frhl19+gaWlZV3WSERERFQlNZoVNW3aNNy/fx8XLlxAVlYWsrKycP78eajVakyfPr2uayQiIiKqkhqdsYmIiMDBgwfRtm1bcZmTkxNCQkJ48TARERFpTY3O2BQXF0NfX7/Ucn19fRQXF9e6KCIiIqKaqFGw6d+/P2bMmIG0tDRx2c2bNzFr1iwMGDCgzoorGfftt99G48aNYWhoCGdnZ5w+fVpsFwQBCxcuRJMmTWBoaAgPDw9cvnxZY4ysrCyMGTMGJiYmMDMzg6+vb6lHP5w7dw4vv/wyDAwMYGtri2XLlpWqZceOHWjTpg0MDAzg7OyM/fv31+m+EhERUe3UKNisWbMGarUazZs3R8uWLdGyZUvY29tDrVbjm2++qbPi7t27h549e0JfXx9//PEH/v77b6xYsQLm5uZin2XLliE4OBjr1q3DyZMnYWRkBE9PT+Tm5op9xowZgwsXLiAqKgrh4eE4duwYJk+eLLar1WoMHDgQdnZ2iIuLw/LlyxEUFIT169eLfU6cOIHRo0fD19cXZ86cgbe3N7y9vXH+/Pk6218iIiKqHZkgCEJNVhQEAQcPHkRSUhIAoG3btvDw8KjT4ubMmYPjx4/jzz//LLcGpVKJDz74AB9++CEAQKVSwdraGmFhYfDx8cHFixfh5OSE2NhYdOnSBcDja4SGDBmCGzduQKlUIjQ0FPPmzUN6ejrkcrm47T179oj7N2rUKOTk5CA8PFzcfvfu3eHi4oJ169ZVaX/UajVMTU2hUqlgYmJS4+NSnmk/HqnzMYnqm2/e7avtEohIC6r6HlqtMzaHDh2Ck5MT1Go1ZDIZXnnlFUybNg3Tpk1D165d0a5du3JDSE3s3bsXXbp0wZtvvgkrKyt06tQJ3333ndienJyM9PR0jUBlamoKNzc3xMTEAHj8/CozMzMx1ACAh4cHdHR0cPLkSbFP7969xVADAJ6enrh06RLu3bsn9nk6uHl6eorbKUteXh7UarXGi4iIiJ6dagWb1atXY9KkSWUmJVNTU7z33ntYuXJlnRV39epVhIaGwsHBAZGRkZg6dSqmT5+OjRs3AgDS09MBANbW1hrrWVtbi23p6emwsrLSaNfT00OjRo00+pQ1xpPbKK9PSXtZli5dClNTU/Fla2tbrf0nIiKi6qlWsDl79iwGDRpUbvvAgQMRFxdX66JKFBcXo3Pnzvj888/RqVMnTJ48GZMmTaryRz/aNnfuXKhUKvF1/fp1bZdEREQkadUKNhkZGWVO8y6hp6eH27dv17qoEk2aNIGTk5PGsrZt2+LatWsAABsbG7Gup+ssabOxsUFmZqZGe2FhIbKysjT6lDXGk9sor09Je1kUCgVMTEw0XkRERPTsVCvYvPTSSxXOAjp37hyaNGlS66JK9OzZE5cuXdJY9s8//8DOzg4AYG9vDxsbG0RHR4vtarUaJ0+ehLu7OwDA3d0d2dnZGmeSDh06hOLiYri5uYl9jh07hoKCArFPVFQUHB0dxRlY7u7uGtsp6VOyHSIiItK+agWbIUOGYMGCBRpTqUs8evQIgYGBePXVV+usuFmzZuGvv/7C559/jitXrmDr1q1Yv349/Pz8AAAymQwzZ87EkiVLsHfvXiQmJmLs2LFQKpXw9vYG8PgMz6BBgzBp0iScOnUKx48fh7+/P3x8fKBUKgEAb731FuRyOXx9fXHhwgVs27YNX3/9NQICAsRaZsyYgYiICKxYsQJJSUkICgrC6dOn4e/vX2f7S0RERLVTreneGRkZ6Ny5M3R1deHv7w9HR0cAQFJSEkJCQlBUVIT4+PhSF9nWRnh4OObOnYvLly/D3t4eAQEBmDRpktguCAICAwOxfv16ZGdno1evXli7di1at24t9snKyoK/vz9+//136OjoYPjw4QgODoaxsbHY59y5c/Dz80NsbCwsLCwwbdo0zJ49W6OWHTt2YP78+UhJSYGDgwOWLVuGIUOGVHlfON2bqPY43ZvoxVTV99Bq38cmNTUVU6dORWRkJEpWlclk8PT0REhICOzt7WtXuYQx2BDVHoMN0Yupqu+h1X4Ipp2dHfbv34979+7hypUrEAQBDg4OGncDJiIiItKGGj3dGwDMzc3RtWvXuqyFiIiIqFZq9KwoIiIiovqIwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCTjPxVsvvjiC8hkMsycOVNclpubCz8/PzRu3BjGxsYYPnw4MjIyNNa7du0avLy80KBBA1hZWeGjjz5CYWGhRp8jR46gc+fOUCgUaNWqFcLCwkptPyQkBM2bN4eBgQHc3Nxw6tSpZ7GbREREVEP/mWATGxuLb7/9Fh06dNBYPmvWLPz+++/YsWMHjh49irS0NLzxxhtie1FREby8vJCfn48TJ05g48aNCAsLw8KFC8U+ycnJ8PLyQr9+/ZCQkICZM2di4sSJiIyMFPts27YNAQEBCAwMRHx8PDp27AhPT09kZmY++50nIiKiKpEJgiBou4jKPHjwAJ07d8batWuxZMkSuLi4YPXq1VCpVLC0tMTWrVsxYsQIAEBSUhLatm2LmJgYdO/eHX/88QdeffVVpKWlwdraGgCwbt06zJ49G7dv34ZcLsfs2bOxb98+nD9/Xtymj48PsrOzERERAQBwc3ND165dsWbNGgBAcXExbG1tMW3aNMyZM6dK+6FWq2FqagqVSgUTE5O6PEQAgGk/HqnzMYnqm2/e7avtEohIC6r6HvqfOGPj5+cHLy8veHh4aCyPi4tDQUGBxvI2bdqgWbNmiImJAQDExMTA2dlZDDUA4OnpCbVajQsXLoh9nh7b09NTHCM/Px9xcXEafXR0dODh4SH2KUteXh7UarXGi4iIiJ4dPW0XUJlffvkF8fHxiI2NLdWWnp4OuVwOMzMzjeXW1tZIT08X+zwZakraS9oq6qNWq/Ho0SPcu3cPRUVFZfZJSkoqt/alS5di0aJFVdtRIiIiqrV6fcbm+vXrmDFjBrZs2QIDAwNtl1Ntc+fOhUqlEl/Xr1/XdklERESSVq+DTVxcHDIzM9G5c2fo6elBT08PR48eRXBwMPT09GBtbY38/HxkZ2drrJeRkQEbGxsAgI2NTalZUiVfV9bHxMQEhoaGsLCwgK6ubpl9SsYoi0KhgImJicaLiIiInp16HWwGDBiAxMREJCQkiK8uXbpgzJgx4r/19fURHR0trnPp0iVcu3YN7u7uAAB3d3ckJiZqzF6KioqCiYkJnJycxD5PjlHSp2QMuVwOV1dXjT7FxcWIjo4W+xAREZH21etg07BhQ7Rv317jZWRkhMaNG6N9+/YwNTWFr68vAgICcPjwYcTFxWHChAlwd3dH9+7dAQADBw6Ek5MT3nnnHZw9exaRkZGYP38+/Pz8oFAoAABTpkzB1atX8fHHHyMpKQlr167F9u3bMWvWLLGWgIAAfPfdd9i4cSMuXryIqVOnIicnBxMmTNDKsSEiIk29e/fG1q1btV3GMxMREQEXFxcUFxdru5R6rV4Hm6pYtWoVXn31VQwfPhy9e/eGjY0Ndu3aJbbr6uoiPDwcurq6cHd3x9tvv42xY8di8eLFYh97e3vs27cPUVFR6NixI1asWIHvv/8enp6eYp9Ro0bhq6++wsKFC+Hi4oKEhARERESUuqCYiOi/JjQ0FB06dBA/Mnd3d8cff/yh0ee9995Dy5YtYWhoCEtLS7z22mulJk9ER0ejR48eaNiwIWxsbDB79myNm6GmpKRAJpOVev31119in127dqFLly4wMzODkZERXFxcsGnTpkr3Ye/evcjIyICPjw8AICsrC9OmTYOjoyMMDQ3RrFkzTJ8+HSqVSmO96dOnw9XVFQqFAi4uLpVupyrjnj17FqNHj4atrS0MDQ3Rtm1bfP311xrjnDlzBp06dYKxsTGGDh2KrKwssa2wsBCurq6lbgI7aNAg6OvrY8uWLZXW+SKr97OinnbkyBGNrw0MDBASEoKQkJBy17Gzs8P+/fsrHLdv3744c+ZMhX38/f3h7+9f5VqJiP4LmjZtii+++AIODg4QBAEbN27Ea6+9hjNnzqBdu3YAAFdXV4wZMwbNmjVDVlYWgoKCMHDgQCQnJ0NXVxdnz57FkCFDMG/ePPz000+4efMmpkyZgqKiInz11Vca2zt48KA4LgA0btxY/HejRo0wb948tGnTBnK5HOHh4ZgwYQKsrKw0/th8WnBwMCZMmAAdncd/r6elpSEtLQ1fffUVnJyckJqaiilTpiAtLQ07d+7UWPfdd9/FyZMnce7cuUqPVVXGjYuLg5WVFTZv3gxbW1ucOHECkydPhq6urvgeMnHiRPTv3x/btm3DxIkT8fnnn4vHacWKFejZsye6detWavvjx49HcHAw3nnnnUprfVH9J27QJxW8QR9R7fEGfc9Ho0aNsHz5cvj6+pbZfu7cOXTs2BFXrlxBy5Yt8cknnyAqKkrj1hy///47Ro4ciczMTDRs2BApKSmwt7fHmTNnqnR2pETnzp3h5eWFTz/9tMz227dvw9raGomJiRqB6Wk7duzA22+/jZycHOjpaf5dHxQUhD179iAhIaHKdVVl3BJ+fn64ePEiDh06BABo0KAB4uPj0aZNG4SGhiI8PBz79u3D1atXMWjQIMTFxaFhw4alxrl27Rrs7OzE4/4ikdQN+oiI6PkoKirCL7/8gpycnHInR+Tk5GDDhg2wt7eHra0tgMc3JH36thyGhobIzc1FXFycxvJhw4bBysoKvXr1wt69e8utRRAEREdH49KlS+jdu3e5/f73v/+hQYMGaNu2bYX7VvKGWF74qKmqjKtSqdCoUSPx644dOyIqKgqFhYWIjo4WHxc0ZcoULFu2rMxQAwDNmjWDtbU1/vzzzzrdBylhsCEiIiQmJsLY2BgKhQJTpkzB7t27xZmjJdauXQtjY2MYGxvjjz/+QFRUFORyOYDHd2s/ceIEfv75ZxQVFeHmzZvitYy3bt0CABgbG2PFihXYsWMH9u3bh169esHb27tUuFGpVDA2NoZcLoeXlxe++eYbvPLKK+XWnpqaCmtra/FjqLLcuXMHn376KSZPnlyj41ObcU+cOIFt27Zp9Pn++++xc+dOtGzZEnK5HHPnzsWmTZvQoEEDdO3aFZ6enmjVqhXmz59fajylUonU1NQ63Q8p+c9dY0NERHXP0dERCQkJUKlU2LlzJ8aNG4ejR49qhJsxY8bglVdewa1bt/DVV19h5MiROH78OAwMDDBw4EAsX74cU6ZMwTvvvAOFQoEFCxbgzz//FAOHhYUFAgICxPG6du2KtLQ0LF++HMOGDROXN2zYEAkJCXjw4AGio6MREBCAFi1aoG/fvmXW/ujRowpv4qpWq+Hl5QUnJycEBQXV7kBVc9zz58/jtddeQ2BgIAYOHCgub9euHY4ePSp+fffuXQQGBuLYsWOYNm0aevTogV27dqFr165wc3PD0KFDxb6GhoZ4+PBhne2H1PCMDRERQS6Xo1WrVnB1dcXSpUvRsWPHUjN5TE1N4eDggN69e2Pnzp1ISkrC7t27xfaAgABkZ2fj2rVruHPnDl577TUAQIsWLcrdrpubG65cuaKxTEdHB61atYKLiws++OADjBgxAkuXLi13DAsLC9y7d6/Mtvv372PQoEFo2LAhdu/eDX19/UqPRVVUZdy///4bAwYMwOTJk8s88/KkgIAAzJw5E02bNsWRI0fw5ptvwsjICF5eXqUmzWRlZcHS0rJO9kOKeMaGiIhKKS4uRl5eXrntgiBAEIRSfWQyGZRKJQDg559/hq2tLTp37lzuOAkJCWjSpEmtaunUqRPS09Nx7949mJubi8vVajU8PT2hUCiwd+/eOns0T1XGvXDhAvr3749x48bhs88+q3C86OhoXLx4ERs2bADw+DqngoICABD/WyI3Nxf//vsvOnXqVCf7IkUMNkREL7i5c+di8ODBaNasGe7fv4+tW7fiyJEjiIyMBABcvXoV27Ztw8CBA2FpaYkbN27giy++gKGhIYYMGSKOs3z5cgwaNAg6OjrYtWsXvvjiC2zfvh26uroAgI0bN0Iul4tvyrt27cKPP/6I77//Xhxj6dKl6NKlC1q2bIm8vDzs378fmzZtQmhoaLn1d+rUCRYWFjh+/DheffVVAI/Dx8CBA/Hw4UNs3rwZarUaarUaAGBpaSnWdOXKFTx48ADp6el49OiROCvKyckJcrkcN2/exIABA/DTTz+hW7duVRr3/Pnz6N+/Pzw9PREQECA+cFlXV7fUmZbc3Fz4+/vj559/Fj+y69mzJ0JCQuDn54dff/0VK1euFPv/9ddfUCgUvOt9BTjd+znidG+i2vsvT/fu/375H6doU9LhX3Hv5r/Iy7kPPbkBjBvboFmn3mhk6wAAyMtRI+nILty/fROFebmQGxrDTNkczV37o4H5/3+jPvPb93hwJw3FRYUwbtwEzbv0R2M7R7H9VlI8riUcRe79bMh0dNDAzBLNXF6GVUtnsc/VkweQ+W8i8h6ooKOnjwZmlmjaoQesW3WocB/+jYlA7oNstHvl8Q367t28ioS935fZt/uYj2BoYv5/NX+H7LTkcvs8Ut/DX1uWw2XYRJi/1KJK4ybHHkTK6UOl2g0amsH97Y816/4rEkJxEVr1+P8B8aHqLv4+uA0Ps2/D2sEFrV8eCpnscei5dHQ3ABkc+3hXeDy06dDauc9k3Kq+hzLYPEcMNkS1x2BDZcl7eB+nflmNrm/6w6CheeUr/AflP8rByZ9XossIPxiaNKp8BS3RdrDhxcNERPSfp2jQEG36DUfu/Wxtl/LM5N6/h9a9X6vXoaY+4DU2REQkCZb2TpV3+g8zsWoKE6um2i6j3uMZGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikox6HWyWLl2Krl27omHDhrCysoK3tzcuXbqk0Sc3Nxd+fn5o3LgxjI2NMXz4cGRkZGj0uXbtGry8vNCgQQNYWVnho48+QmFhoUafI0eOoHPnzlAoFGjVqhXCwsJK1RMSEoLmzZvDwMAAbm5uOHXqVJ3vMxEREdVcvQ42R48ehZ+fH/766y9ERUWhoKAAAwcORE5Ojthn1qxZ+P3337Fjxw4cPXoUaWlpeOONN8T2oqIieHl5IT8/HydOnMDGjRsRFhaGhQsXin2Sk5Ph5eWFfv36ISEhATNnzsTEiRMRGRkp9tm2bRsCAgIQGBiI+Ph4dOzYEZ6ensjMzHw+B4OIiIgqJRMEQdB2EVV1+/ZtWFlZ4ejRo+jduzdUKhUsLS2xdetWjBgxAgCQlJSEtm3bIiYmBt27d8cff/yBV199FWlpabC2tgYArFu3DrNnz8bt27chl8sxe/Zs7Nu3D+fPnxe35ePjg+zsbERERAAA3Nzc0LVrV6xZswYAUFxcDFtbW0ybNg1z5sypUv1qtRqmpqZQqVQwMTGpy0MDAJj245E6H5Oovvnm3b7aLqHG+r+/VNslED1zh9bOfSbjVvU9tF6fsXmaSqUCADRq1AgAEBcXh4KCAnh4eIh92rRpg2bNmiEmJgYAEBMTA2dnZzHUAICnpyfUajUuXLgg9nlyjJI+JWPk5+cjLi5Oo4+Ojg48PDzEPmXJy8uDWq3WeBEREdGz858JNsXFxZg5cyZ69uyJ9u3bAwDS09Mhl8thZmam0dfa2hrp6elinydDTUl7SVtFfdRqNR49eoQ7d+6gqKiozD4lY5Rl6dKlMDU1FV+2trbV33EiIiKqsv9MsPHz88P58+fxyy+/aLuUKps7dy5UKpX4un79urZLIiIikjQ9bRdQFf7+/ggPD8exY8fQtGlTcbmNjQ3y8/ORnZ2tcdYmIyMDNjY2Yp+nZy+VzJp6ss/TM6kyMjJgYmICQ0ND6OrqQldXt8w+JWOURaFQQKFQVH+HiYiIqEbq9RkbQRDg7++P3bt349ChQ7C3t9dod3V1hb6+PqKjo8Vlly5dwrVr1+Du7g4AcHd3R2JiosbspaioKJiYmMDJyUns8+QYJX1KxpDL5XB1ddXoU1xcjOjoaLEPERERaV+9PmPj5+eHrVu34rfffkPDhg3F61lMTU1haGgIU1NT+Pr6IiAgAI0aNYKJiQmmTZsGd3d3dO/eHQAwcOBAODk54Z133sGyZcuQnp6O+fPnw8/PTzybMmXKFKxZswYff/wx3n33XRw6dAjbt2/Hvn37xFoCAgIwbtw4dOnSBd26dcPq1auRk5ODCRMmPP8DQ0RERGWq18EmNDQUANC3b1+N5Rs2bMD48eMBAKtWrYKOjg6GDx+OvLw8eHp6Yu3atWJfXV1dhIeHY+rUqXB3d4eRkRHGjRuHxYsXi33s7e2xb98+zJo1C19//TWaNm2K77//Hp6enmKfUaNG4fbt21i4cCHS09Ph4uKCiIiIUhcUExERkfb8p+5j81/H+9gQ1R7vY0NUv/E+NkRERER1hMGGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwYaIiIgkg8GGiIiIJIPBhoiIiCSDwaaaQkJC0Lx5cxgYGMDNzQ2nTp3SdklERET0fxhsqmHbtm0ICAhAYGAg4uPj0bFjR3h6eiIzM1PbpREREREYbKpl5cqVmDRpEiZMmAAnJyesW7cODRo0wI8//qjt0oiIiAiAnrYL+K/Iz89HXFwc5s6dKy7T0dGBh4cHYmJiylwnLy8PeXl54tcqlQoAoFarn02Nj3KeybhE9cmz+v15Hgrzc7VdAtEz96x+R0vGFQShwn4MNlV0584dFBUVwdraWmO5tbU1kpKSylxn6dKlWLRoUanltra2z6RGohfBen9tV0BEFTH9YfEzHf/+/fswNTUtt53B5hmaO3cuAgICxK+Li4uRlZWFxo0bQyaTabEyqgtqtRq2tra4fv06TExMtF0OET2Fv6PSIggC7t+/D6VSWWE/BpsqsrCwgK6uLjIyMjSWZ2RkwMbGpsx1FAoFFAqFxjIzM7NnVSJpiYmJCf+nSVSP8XdUOio6U1OCFw9XkVwuh6urK6Kjo8VlxcXFiI6Ohru7uxYrIyIiohI8Y1MNAQEBGDduHLp06YJu3bph9erVyMnJwYQJE7RdGhEREYHBplpGjRqF27dvY+HChUhPT4eLiwsiIiJKXVBMLwaFQoHAwMBSHzcSUf3A39EXk0yobN4UERER0X8Er7EhIiIiyWCwISIiIslgsCEiIiLJYLAhIiIiyWCwIaqhkJAQNG/eHAYGBnBzc8OpU6e0XRIRATh27BiGDh0KpVIJmUyGPXv2aLskeo4YbIhqYNu2bQgICEBgYCDi4+PRsWNHeHp6IjMzU9ulEb3wcnJy0LFjR4SEhGi7FNICTvcmqgE3Nzd07doVa9asAfD4LtS2traYNm0a5syZo+XqiKiETCbD7t274e3tre1S6DnhGRuiasrPz0dcXBw8PDzEZTo6OvDw8EBMTIwWKyMiIgYbomq6c+cOioqKSt1x2traGunp6VqqioiIAAYbIiIikhAGG6JqsrCwgK6uLjIyMjSWZ2RkwMbGRktVERERwGBDVG1yuRyurq6Ijo4WlxUXFyM6Ohru7u5arIyIiPh0b6IaCAgIwLhx49ClSxd069YNq1evRk5ODiZMmKDt0oheeA8ePMCVK1fEr5OTk5GQkIBGjRqhWbNmWqyMngdO9yaqoTVr1mD58uVIT0+Hi4sLgoOD4ebmpu2yiF54R44cQb9+/UotHzduHMLCwp5/QfRcMdgQERGRZPAaGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbInqhyGQy7NmzR9tlENEzwmBDRJKSnp6OadOmoUWLFlAoFLC1tcXQoUM1HlpKRNLFh2ASkWSkpKSgZ8+eMDMzw/Lly+Hs7IyCggJERkbCz88PSUlJ2i6RiJ4xnrEhIsl4//33IZPJcOrUKQwfPhytW7dGu3btEBAQgL/++qvMdWbPno3WrVujQYMGaNGiBRYsWICCggKx/ezZs+jXrx8aNmwIExMTuLq64vTp0wCA1NRUDB06FObm5jAyMkK7du2wf//+57KvRFQ2nrEhIknIyspCREQEPvvsMxgZGZVqNzMzK3O9hg0bIiwsDEqlEomJiZg0aRIaNmyIjz/+GAAwZswYdOrUCaGhodDV1UVCQgL09fUBAH5+fsjPz8exY8dgZGSEv//+G8bGxs9sH4mocgw2RCQJV65cgSAIaNOmTbXWmz9/vvjv5s2b48MPP8Qvv/wiBptr167ho48+Esd1cHAQ+1+7dg3Dhw+Hs7MzAKBFixa13Q0iqiV+FEVEkiAIQo3W27ZtG3r27AkbGxsYGxtj/vz5uHbtmtgeEBCAiRMnwsPDA1988QX+/fdfsW369OlYsmQJevbsicDAQJw7d67W+0FEtcNgQ0SS4ODgAJlMVq0LhGNiYjBmzBgMGTIE4eHhOHPmDObNm4f8/HyxT1BQEC5cuAAvLy8cOnQITk5O2L17NwBg4sSJuHr1Kt555x0kJiaiS5cu+Oabb+p834io6mRCTf/MISKqZwYPHozExERcunSp1HU22dnZMDMzg0wmw+7du+Ht7Y0VK1Zg7dq1GmdhJk6ciJ07dyI7O7vMbYwePRo5OTnYu3dvqba5c+di3759PHNDpEU8Y0NEkhESEoKioiJ069YNv/76Ky5fvoyLFy8iODgY7u7upfo7ODjg2rVr+OWXX/Dvv/8iODhYPBsDAI8ePYK/vz+OHDmC1NRUHD9+HLGxsWjbti0AYObMmYiMjERycjLi4+Nx+PBhsY2ItIMXDxORZLRo0QLx8fH47LPP8MEHH+DWrVuwtLSEq6srQkNDS/UfNmwYZs2aBX9/f+Tl5cHLywsLFixAUFAQAEBXVxd3797F2LFjkZGRAQsLC7zxxhtYtGgRAKCoqAh+fn64ceMGTExMMGjQIKxatep57jIRPYUfRREREZFk8KMoIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpIMBhsiIiKSDAYbIiIikgwGGyIiIpKM/wcQ/g/mE/oB7gAAAABJRU5ErkJggg==\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 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\n"},"metadata":{}},{"name":"stdout","text":"\n=== (Validation Set)_3 ===\nAccuracy: 0.8436\nAUC: 0.8928\n\n分类报告:\n              precision    recall  f1-score   support\n\n           0       0.97      0.85      0.91     35980\n           1       0.37      0.76      0.49      4020\n\n    accuracy                           0.84     40000\n   macro avg       0.67      0.81      0.70     40000\nweighted avg       0.91      0.84      0.87     40000\n\n1500:\ttest: 0.8930132\tbest: 0.8930234 (1498)\ttotal: 1m 36s\tremaining: 9m 3s\n1600:\ttest: 0.8935684\tbest: 0.8935802 (1599)\ttotal: 1m 42s\tremaining: 8m 57s\n1700:\ttest: 0.8941843\tbest: 0.8941872 (1696)\ttotal: 1m 48s\tremaining: 8m 51s\n1800:\ttest: 0.8946735\tbest: 0.8946735 (1800)\ttotal: 1m 55s\tremaining: 8m 45s\n1900:\ttest: 0.8952222\tbest: 0.8952222 (1900)\ttotal: 2m 1s\tremaining: 8m 39s\n2000:\ttest: 0.8956242\tbest: 0.8956564 (1989)\ttotal: 2m 8s\tremaining: 8m 32s\n2100:\ttest: 0.8960683\tbest: 0.8960683 (2100)\ttotal: 2m 14s\tremaining: 8m 26s\n2200:\ttest: 0.8963992\tbest: 0.8963992 (2200)\ttotal: 2m 21s\tremaining: 8m 19s\n2300:\ttest: 0.8966143\tbest: 0.8966143 (2300)\ttotal: 2m 27s\tremaining: 8m 13s\n2400:\ttest: 0.8968263\tbest: 0.8968328 (2399)\ttotal: 2m 33s\tremaining: 8m 6s\n2500:\ttest: 0.8968840\tbest: 0.8969038 (2490)\ttotal: 2m 40s\tremaining: 8m\n2600:\ttest: 0.8969920\tbest: 0.8970184 (2555)\ttotal: 2m 46s\tremaining: 7m 53s\n2700:\ttest: 0.8973005\tbest: 0.8973005 (2700)\ttotal: 2m 52s\tremaining: 7m 47s\n2800:\ttest: 0.8974175\tbest: 0.8974218 (2762)\ttotal: 2m 59s\tremaining: 7m 40s\n2900:\ttest: 0.8975161\tbest: 0.8975299 (2895)\ttotal: 3m 5s\tremaining: 7m 34s\n3000:\ttest: 0.8975136\tbest: 0.8975382 (2929)\ttotal: 3m 12s\tremaining: 7m 27s\n3100:\ttest: 0.8976042\tbest: 0.8976278 (3085)\ttotal: 3m 18s\tremaining: 7m 21s\n3200:\ttest: 0.8976175\tbest: 0.8976439 (3195)\ttotal: 3m 24s\tremaining: 7m 14s\n3300:\ttest: 0.8976365\tbest: 0.8976519 (3247)\ttotal: 3m 31s\tremaining: 7m 8s\n3400:\ttest: 0.8976709\tbest: 0.8977200 (3379)\ttotal: 3m 37s\tremaining: 7m 1s\n3500:\ttest: 0.8977979\tbest: 0.8978031 (3499)\ttotal: 3m 43s\tremaining: 6m 55s\n3600:\ttest: 0.8977909\tbest: 0.8978133 (3587)\ttotal: 3m 50s\tremaining: 6m 48s\n3700:\ttest: 0.8978418\tbest: 0.8978426 (3699)\ttotal: 3m 56s\tremaining: 6m 42s\n3800:\ttest: 0.8978793\tbest: 0.8978811 (3798)\ttotal: 4m 2s\tremaining: 6m 35s\n3900:\ttest: 0.8979064\tbest: 0.8979308 (3871)\ttotal: 4m 9s\tremaining: 6m 29s\nStopped by overfitting detector  (100 iterations wait)\n\nbestTest = 0.8979308018\nbestIteration = 3871\n\nShrink model to first 3872 iterations.\n\n=== (Train Set)_4 ===\nAccuracy: 0.8622\nAUC: 0.9368\n\n分类报告:\n              precision    recall  f1-score   support\n\n           0       0.98      0.86      0.92    143922\n           1       0.41      0.86      0.56     16078\n\n    accuracy                           0.86    160000\n   macro avg       0.70      0.86      0.74    160000\nweighted avg       0.93      0.86      0.88    160000\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 600x400 with 1 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 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\n"},"metadata":{}},{"name":"stdout","text":"\n=== (Validation Set)_4 ===\nAccuracy: 0.8457\nAUC: 0.8979\n\n分类报告:\n              precision    recall  f1-score   support\n\n           0       0.97      0.85      0.91     35980\n           1       0.37      0.78      0.50      4020\n\n    accuracy                           0.85     40000\n   macro avg       0.67      0.82      0.71     40000\nweighted avg       0.91      0.85      0.87     40000\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 600x400 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 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\n"},"metadata":{}},{"name":"stdout","text":"\n===== Fold 5/5 =====\n0:\ttest: 0.5586654\tbest: 0.5586654 (0)\ttotal: 76.8ms\tremaining: 12m 48s\n100:\ttest: 0.8091277\tbest: 0.8091277 (100)\ttotal: 6.65s\tremaining: 10m 52s\n200:\ttest: 0.8361017\tbest: 0.8361017 (200)\ttotal: 13s\tremaining: 10m 33s\n300:\ttest: 0.8549524\tbest: 0.8552905 (299)\ttotal: 19.2s\tremaining: 10m 20s\n400:\ttest: 0.8650666\tbest: 0.8650666 (400)\ttotal: 25.6s\tremaining: 10m 13s\n500:\ttest: 0.8722557\tbest: 0.8722557 (500)\ttotal: 32.1s\tremaining: 10m 8s\n600:\ttest: 0.8772732\tbest: 0.8772732 (600)\ttotal: 38.6s\tremaining: 10m 3s\n700:\ttest: 0.8810198\tbest: 0.8810198 (700)\ttotal: 45s\tremaining: 9m 57s\n800:\ttest: 0.8841542\tbest: 0.8841542 (800)\ttotal: 51.4s\tremaining: 9m 50s\n900:\ttest: 0.8865930\tbest: 0.8865930 (900)\ttotal: 57.9s\tremaining: 9m 44s\n1000:\ttest: 0.8884192\tbest: 0.8884192 (1000)\ttotal: 1m 4s\tremaining: 9m 39s\n1100:\ttest: 0.8901240\tbest: 0.8901240 (1100)\ttotal: 1m 10s\tremaining: 9m 32s\n1200:\ttest: 0.8918258\tbest: 0.8918258 (1200)\ttotal: 1m 17s\tremaining: 9m 27s\n1300:\ttest: 0.8929492\tbest: 0.8929647 (1299)\ttotal: 1m 23s\tremaining: 9m 21s\n1400:\ttest: 0.8938578\tbest: 0.8938578 (1400)\ttotal: 1m 30s\tremaining: 9m 15s\n1500:\ttest: 0.8948320\tbest: 0.8948320 (1500)\ttotal: 1m 37s\tremaining: 9m 9s\n1600:\ttest: 0.8952032\tbest: 0.8952269 (1595)\ttotal: 1m 43s\tremaining: 9m 2s\n1700:\ttest: 0.8958913\tbest: 0.8958913 (1700)\ttotal: 1m 50s\tremaining: 8m 57s\n1800:\ttest: 0.8963064\tbest: 0.8963225 (1799)\ttotal: 1m 56s\tremaining: 8m 52s\n1900:\ttest: 0.8966156\tbest: 0.8966157 (1899)\ttotal: 2m 3s\tremaining: 8m 46s\n2000:\ttest: 0.8972012\tbest: 0.8972023 (1981)\ttotal: 2m 10s\tremaining: 8m 40s\n2100:\ttest: 0.8976831\tbest: 0.8976873 (2099)\ttotal: 2m 16s\tremaining: 8m 34s\n2200:\ttest: 0.8979836\tbest: 0.8979962 (2199)\ttotal: 2m 23s\tremaining: 8m 27s\n2300:\ttest: 0.8981166\tbest: 0.8981166 (2300)\ttotal: 2m 29s\tremaining: 8m 20s\n2400:\ttest: 0.8983562\tbest: 0.8983562 (2400)\ttotal: 2m 36s\tremaining: 8m 14s\n2500:\ttest: 0.8986064\tbest: 0.8986064 (2500)\ttotal: 2m 42s\tremaining: 8m 7s\n2600:\ttest: 0.8986815\tbest: 0.8987145 (2568)\ttotal: 2m 49s\tremaining: 8m\nStopped by overfitting detector  (100 iterations wait)\n\nbestTest = 0.8987145429\nbestIteration = 2568\n\nShrink model to first 2569 iterations.\n\n=== (Train Set)_5 ===\nAccuracy: 0.8546\nAUC: 0.9283\n\n分类报告:\n              precision    recall  f1-score   support\n\n           0       0.98      0.86      0.91    143922\n           1       0.40      0.85      0.54     16078\n\n    accuracy                           0.85    160000\n   macro avg       0.69      0.85      0.73    160000\nweighted avg       0.92      0.85      0.88    160000\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 600x400 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 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\n"},"metadata":{}},{"name":"stdout","text":"\n=== (Validation Set)_5 ===\nAccuracy: 0.8381\nAUC: 0.8987\n\n分类报告:\n              precision    recall  f1-score   support\n\n           0       0.97      0.84      0.90     35980\n           1       0.36      0.79      0.49      4020\n\n    accuracy                           0.84     40000\n   macro avg       0.67      0.82      0.70     40000\nweighted avg       0.91      0.84      0.86     40000\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 600x400 with 1 Axes>","image/png":"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size 640x480 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 2 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\n"},"metadata":{}}],"execution_count":23},{"cell_type":"markdown","source":"# 特征重要性","metadata":{}},{"cell_type":"code","source":"# ====== 特征重要性（如需可关闭/挪到外部）======\ni=0\nfor catboost_model in catboost_models:\n    i+=1\n   \n    plot_feature_importance(catboost_model, top_n=20, save_path=f\"/kaggle/working/catboost_evaluation/cat_feature_importance_{i}.png\")","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 非集成的加权平均预测test结果","metadata":{}},{"cell_type":"markdown","source":"简单相加和加权平均的结果是一样的","metadata":{}},{"cell_type":"code","source":"# 计算每个模型的权重，权重与 AUC 成正比\nweights = np.array(fold_aucs) / np.sum(fold_aucs)  # 归一化 AUC 分数，使得权重总和为 1\nprint(weights)\n# 初始化加权预测结果\ntest_pred = np.zeros(len(X_test))\n\n# 对每个模型的预测结果进行加权\nfor model, weight in zip(catboost_models, weights):\n    test_pred += model.predict_proba(X_test)[:, 1] * weight","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T15:08:20.758931Z","iopub.execute_input":"2025-09-16T15:08:20.759405Z","iopub.status.idle":"2025-09-16T15:08:29.000524Z","shell.execute_reply.started":"2025-09-16T15:08:20.759377Z","shell.execute_reply":"2025-09-16T15:08:28.999290Z"}},"outputs":[{"name":"stdout","text":"[0.20037945 0.20028342 0.19896135 0.20010056 0.20027522]\n","output_type":"stream"}],"execution_count":24},{"cell_type":"markdown","source":"# 输出预测结果","metadata":{}},{"cell_type":"code","source":"sub = pd.read_csv('/kaggle/input/santander-customer-transaction-prediction/sample_submission.csv')\nsub['target'] = 0.0\n# 对于 real_samples_indexes 中的索引，填充预测值\nsub.loc[real_samples_indexes, 'target'] = test_pred\nsub.to_csv('/kaggle/working/submission.csv',index=False)\nsub.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-16T15:08:29.002027Z","iopub.execute_input":"2025-09-16T15:08:29.002387Z","iopub.status.idle":"2025-09-16T15:08:29.496941Z","shell.execute_reply.started":"2025-09-16T15:08:29.002356Z","shell.execute_reply":"2025-09-16T15:08:29.496072Z"}},"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"  ID_code    target\n0  test_0  0.000000\n1  test_1  0.000000\n2  test_2  0.000000\n3  test_3  0.594322\n4  test_4  0.000000","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID_code</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>test_0</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>test_1</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>test_2</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>test_3</td>\n      <td>0.594322</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>test_4</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":25},{"cell_type":"markdown","source":"# 划分训练和验证便于后续调参","metadata":{}},{"cell_type":"markdown","source":"调参分为2步，第一步是快速调参，将会在train集中抽取15%的数据用于调参，在val上进行验证，第二步是全量验证，将会运用调好的参数在所有数据上进行运行训练。","metadata":{}},{"cell_type":"code","source":"x_train, x_val, y_train, y_val = train_test_split(train_x, train_y, test_size=0.2, random_state=42)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 随机网格搜索调优","metadata":{}},{"cell_type":"code","source":"# --- 设备参数：CatBoost 用 task_type 控制 ---\ndef _device_params_cat(use_gpu: bool):\n    \"\"\"\n    CatBoost:\n      - CPU: task_type='CPU'\n      - GPU: task_type='GPU'（若无GPU会自动回退，但建议仅在确有GPU时开启）\n    \"\"\"\n    if use_gpu:\n        return {\"task_type\": \"GPU\", \"devices\": \"0\"}  # 多卡时可改 \"0,1\"\n    else:\n        return {\"task_type\": \"CPU\"}\n\ndef stratified_subsample(X, y, frac=0.1, random_state=42):\n    \"\"\"分层抽样：用于快速搜参（只抽一次）\"\"\"\n    X_sub, _, y_sub, _ = train_test_split(\n        X, y, test_size=1 - frac, stratify=y, random_state=random_state\n    )\n    return X_sub, y_sub\n\ndef quick_tune_catboost(\n    X_train, y_train, X_val, y_val, \n    n_iter=25, random_state=42, use_gpu=False, frac=0.15,\n    cat_features=None\n):\n    \"\"\"\n    在小样本上快速搜参（CatBoost 版），返回最优参数 best_params\n    - 固定验证集做早停（eval_set=X_val,y_val），CV用 PredefinedSplit 保证“训练=子样本、验证=外部验证”\n    - 支持类别不平衡（class_weights）\n    \"\"\"\n    # —— 子样本（搜参更快） ——\n    X_sub, y_sub = stratified_subsample(X_train, y_train, frac=frac, random_state=random_state)\n\n    # 兼容 pandas / numpy\n    X_sub_np = X_sub.values if hasattr(X_sub, \"values\") else np.asarray(X_sub)\n    X_val_np = X_val.values if hasattr(X_val, \"values\") else np.asarray(X_val)\n    y_sub_np = np.asarray(y_sub)\n    y_val_np = np.asarray(y_val)\n\n    # —— 固定验证折：子样本为训练(-1)，外部验证为验证(0) ——\n    X_all = np.vstack([X_sub_np, X_val_np])\n    y_all = np.concatenate([y_sub_np, y_val_np])\n    test_fold = np.concatenate([\n        -1 * np.ones(len(X_sub_np), dtype=int),\n         0 * np.ones(len(X_val_np), dtype=int)\n    ])\n    ps = PredefinedSplit(test_fold)\n\n    # —— 类别不平衡权重（基于子样本） ——\n    pos = np.sum(y_sub_np == 1)\n    neg = np.sum(y_sub_np == 0)\n    class_weights = [1.0, float(neg)/float(pos)] if pos > 0 else [1.0, 1.0]\n\n    # —— 基础模型（尽量与XGB版本配置语义对应） ——\n    base = CatBoostClassifier(\n        loss_function='Logloss',\n        eval_metric='AUC',          # 与 scoring 一致\n        random_state=random_state,\n        verbose=False,              # 搜参时静默，由CV控制\n        class_weights=class_weights,\n        od_type='Iter',             # 早停\n        od_wait=100,                # patience\n        **_device_params_cat(use_gpu)\n    )\n\n    # —— 超参搜索空间（CatBoost 常用超参） ——\n    param_dist = {\n        \"iterations\":        [1000, 2000, 3000,5000]\n        \"learning_rate\":     [0.03, 0.05, 0.07, 0.1],\n        \"depth\":             [3,4, 5, 6, 8],\n        \"l2_leaf_reg\":       [1.0, 3.0, 5.0, 7.0, 10.0],\n        \"subsample\":         [0.7, 0.8, 0.9, 1.0],\n        \"random_strength\":   [1.0, 2.0, 5.0, 10.0],\n        \"bagging_temperature\":[0.0, 0.5, 1.0]\n    }\n    # GPU/CPU 均可用上述超参；如在GPU上可考虑增加 depth<=10、使用默认边界数\n\n    # —— 搜参器（评分用 AUC；CV 按 ps 划分） ——\n    search = RandomizedSearchCV(\n        estimator=base,\n        param_distributions=param_dist,\n        n_iter=n_iter,\n        scoring=\"roc_auc\",\n        cv=ps,\n        verbose=1,\n        n_jobs=-1,\n        random_state=random_state,\n        refit=True\n    )\n\n    # —— 早停的 eval_set 仍然指向外部验证集；CatBoost 支持 sklearn 接口的 fit_params ——\n    fit_params = {\n        \"eval_set\": (X_val_np, y_val_np),\n        \"use_best_model\": True\n    }\n    # 若有类别特征，建议传 Pool（在最终训练里更关键，这里可先简单用 numpy）\n    # 也可以将 cat_features 直接放在 estimator 里：CatBoostClassifier(..., cat_features=cat_features)\n    if cat_features is not None:\n        # sklearn API 下直接传 numpy 也能用；更严谨可改用 Pool 训练，这里保持简洁\n        search.estimator.set_params(cat_features=cat_features)\n\n    search.fit(X_all, y_all, **fit_params)\n\n    print(\"快速搜参最优参数：\", search.best_params_)\n    print(\"快速搜参最佳AUC：\", search.best_score_)\n    return search.best_params_\n\ndef train_full_catboost(\n    X_train, y_train, X_val, y_val, \n    best_params, random_state=42, use_gpu=False, cat_features=None, verbose=100\n):\n    \"\"\"\n    用全量训练集进行最终训练，并基于外部验证集早停；返回训练好的 CatBoost 模型\n    \"\"\"\n    # 转 numpy（CatBoost 也可直接喂 DataFrame；这里与上游保持一致）\n    X_train_np = X_train.values if hasattr(X_train, \"values\") else np.asarray(X_train)\n    X_val_np   = X_val.values   if hasattr(X_val, \"values\")   else np.asarray(X_val)\n    y_train_np = np.asarray(y_train)\n    y_val_np   = np.asarray(y_val)\n\n    # 类别不平衡（用全量数据重算更稳妥）\n    pos = np.sum(y_train_np == 1)\n    neg = np.sum(y_train_np == 0)\n    class_weights = [1.0, float(neg)/float(pos)] if pos > 0 else [1.0, 1.0]\n\n    model = CatBoostClassifier(\n        loss_function='Logloss',\n        eval_metric='AUC',\n        random_state=random_state,\n        class_weights=class_weights,\n        od_type='Iter',\n        od_wait=200,                 # 全量训练可稍大一点\n        verbose=verbose,\n        **_device_params_cat(use_gpu),\n        **best_params\n    )\n\n    # 若提供了类别特征，优先用 Pool（CatBoost 对分类特征处理更稳）\n    if cat_features is not None:\n        train_pool = Pool(X_train_np, y_train_np, cat_features=cat_features)\n        val_pool   = Pool(X_val_np,   y_val_np,   cat_features=cat_features)\n        model.fit(train_pool, eval_set=val_pool, use_best_model=True)\n        val_auc = roc_auc_score(y_val_np, model.predict_proba(val_pool)[:, 1])\n    else:\n        model.fit(X_train_np, y_train_np, eval_set=(X_val_np, y_val_np), use_best_model=True)\n        val_auc = roc_auc_score(y_val_np, model.predict_proba(X_val_np)[:, 1])\n\n    print(\"全量精修 AUC:\", val_auc)\n    return model\n","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# # 1) 快速搜参（在子样本上）\n# best_params = quick_tune_catboost(\n#     x_train, y_train, x_val, y_val,\n#     n_iter=25, random_state=42, use_gpu=False, frac=0.15,\n#     cat_features=None  # 如果有分类特征索引/列名，传这里\n# )\n\n# # 2) 全量训练（带早停）\n# cat_model = train_full_catboost(\n#     x_train, y_train, x_val, y_val,\n#     best_params=best_params,\n#     random_state=42,\n#     use_gpu=False,\n#     cat_features=None,   # 有就传\n#     verbose=100\n# )","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# optuna搜索调优","metadata":{}},{"cell_type":"code","source":"import optuna\nimport numpy as np\nfrom catboost import CatBoostClassifier, Pool\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.model_selection import train_test_split\n\n# 用于设备的参数设置（GPU / CPU）\ndef _device_params_cat(use_gpu: bool):\n    if use_gpu:\n        return {\"task_type\": \"GPU\", \"devices\": \"0\"}  # 使用 GPU，0表示单卡\n    else:\n        return {\"task_type\": \"CPU\"}\n\ndef stratified_subsample(X, y, frac=0.1, random_state=42):\n    \"\"\"分层抽样：用于快速搜参（只抽一次）\"\"\"\n    X_sub, _, y_sub, _ = train_test_split(\n        X, y, test_size=1 - frac, stratify=y, random_state=random_state\n    )\n    return X_sub, y_sub\n\n# 目标函数，用于 Optuna 优化 CatBoost 的超参数\ndef objective(trial, X_train, y_train, X_val, y_val, cat_features=None, use_gpu=False, frac=0.15):\n    \"\"\"\n    用于 Optuna 优化 CatBoost 超参数的目标函数\n    \"\"\"\n    # 子样本（搜参更快）\n    X_sub, y_sub = stratified_subsample(X_train, y_train, frac=frac, random_state=42)\n\n    # 超参数空间\n    param = {\n    \"iterations\": trial.suggest_int('iterations', 1000, 5000, step=1000),\n    \"learning_rate\": trial.suggest_float('learning_rate', 0.01, 0.1),\n    \"depth\": trial.suggest_int('depth', 2, 4),\n    \"l2_leaf_reg\": trial.suggest_float('l2_leaf_reg', 1.0, 10.0),\n    \"subsample\": trial.suggest_float('subsample', 0.7, 1.0),  # 子样本比例\n    \"random_strength\": trial.suggest_float('random_strength', 1.0, 10.0),\n    \"min_data_in_leaf\": trial.suggest_int('min_data_in_leaf', 1, 10),  # 每个叶子节点的最小数据量\n    \"max_bin\": trial.suggest_int('max_bin', 100, 500),  # 最大分箱数\n    \"grow_policy\": trial.suggest_categorical('grow_policy', ['Depthwise', 'Lossguide']),  # 树的生长策略\n    \"leaf_estimation_iterations\": trial.suggest_int('leaf_estimation_iterations', 1, 10),  # 叶子节点估计次数\n    \"score_function\": trial.suggest_categorical('score_function', ['L2', 'Cosine']),  # 得分函数\n    \"bootstrap_type\": \"Bernoulli\",  # 使用 Bernoulli bootstrap\n    \"max_ctr_complexity\": trial.suggest_int('max_ctr_complexity', 1, 4),  # 最大类别特征组合复杂度\n    }\n\n\n    # 类别不平衡（基于子样本）\n    pos = np.sum(y_sub == 1)\n    neg = np.sum(y_sub == 0)\n    class_weights = [1.0, float(neg)/float(pos)] if pos > 0 else [1.0, 1.0]\n\n    # 创建 CatBoostClassifier\n    model = CatBoostClassifier(\n        loss_function='Logloss',\n        eval_metric='AUC',\n        random_state=42,\n        class_weights=class_weights,\n        od_type='Iter',\n        od_wait=100,  # 早停时的 patience\n        verbose=False,  # 搜参时静默输出\n        **_device_params_cat(use_gpu),\n        **param\n    )\n\n    # 训练模型\n    model.fit(X_sub, y_sub, eval_set=(X_val, y_val), use_best_model=True, verbose=False)\n\n    # 获取验证集的预测 AUC\n    val_auc = roc_auc_score(y_val, model.predict_proba(X_val)[:, 1])\n\n    return val_auc  # 目标是最大化 AUC\n\n# 调用 Optuna 进行超参数调优\ndef tune_catboost(X_train, y_train, X_val, y_val, cat_features=None, use_gpu=False, n_trials=25):\n    \"\"\"\n    使用 Optuna 优化 CatBoost 的超参数\n    \"\"\"\n    study = optuna.create_study(direction='maximize')  # 目标是最大化 AUC\n    study.optimize(lambda trial: objective(trial, X_train, y_train, X_val, y_val, cat_features, use_gpu), n_trials=n_trials)\n\n    print(\"Best parameters:\", study.best_params)\n    print(\"Best AUC:\", study.best_value)\n    return study.best_params\n\n# 使用全量训练集训练最终的 CatBoost 模型\ndef train_full_catboost(X_train, y_train, X_val, y_val, best_params, use_gpu=False, cat_features=None, verbose=100):\n    \"\"\"\n    用全量训练集进行最终训练，并基于外部验证集早停；返回训练好的 CatBoost 模型\n    \"\"\"\n    X_train_np = X_train.values if hasattr(X_train, \"values\") else np.asarray(X_train)\n    X_val_np = X_val.values if hasattr(X_val, \"values\") else np.asarray(X_val)\n    y_train_np = np.asarray(y_train)\n    y_val_np = np.asarray(y_val)\n\n    # 类别不平衡（用全量数据重算更稳妥）\n    pos = np.sum(y_train_np == 1)\n    neg = np.sum(y_train_np == 0)\n    class_weights = [1.0, float(neg)/float(pos)] if pos > 0 else [1.0, 1.0]\n\n    # 创建最终模型\n    model = CatBoostClassifier(\n        loss_function='Logloss',\n        eval_metric='AUC',\n        random_state=42,\n        class_weights=class_weights,\n        od_type='Iter',\n        od_wait=200,  # 全量训练时可以稍大一些\n        verbose=verbose,\n        bootstrap_type=\"Bernoulli\",\n        **_device_params_cat(use_gpu),\n        **best_params\n    )\n\n    # 使用 Pool 训练以提高性能（针对类别特征）\n    if cat_features is not None:\n        train_pool = Pool(X_train_np, y_train_np, cat_features=cat_features)\n        val_pool = Pool(X_val_np, y_val_np, cat_features=cat_features)\n        model.fit(train_pool, eval_set=val_pool, use_best_model=True)\n        val_auc = roc_auc_score(y_val_np, model.predict_proba(val_pool)[:, 1])\n    else:\n        model.fit(X_train_np, y_train_np, eval_set=(X_val_np, y_val_np), use_best_model=True)\n        val_auc = roc_auc_score(y_val_np, model.predict_proba(X_val_np)[:, 1])\n\n    print(\"Final model AUC:\", val_auc)\n    return model\n","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# 假设你已经准备好了 X_train, y_train, X_val, y_val\nbest_params = tune_catboost(x_train, y_train, x_val, y_val, cat_features=None, use_gpu=True, n_trials=50)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# 使用最佳超参数在全量数据上训练最终的 CatBoost 模型\nfinal_model = train_full_catboost(x_train, y_train, x_val, y_val, best_params, use_gpu=True, cat_features=None, verbose=100)","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}